Back| V- |
| validity validity passed a value of -1. 3) the parameter value returned by pjlaenv is checked for validity retrieve the optimal blocksize for strtri as follows: |
| value value ccombamax1 finds the element having maximum real part absolute value as well as its corresponding globl index arguments = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. each row and column of the distributed matrix b with elements b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1 r(i) and c(j) are restricted to be between smlnum = smallest safe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pcgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pcheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pcheevd computes all the eigenvalues and eigenvectors of a hermitia pcheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pchegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form bal, but the handle (the integer value) may vary array a. the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes find a value for rot pclange returns the value of the one norm, or the frobenius norm distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1). bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pclassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. scale x so that its components are less than or equal to bignum in absolute value bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pcmax1 computes the global index of the maximum element in absolute value of a distributed vector sub( x ). the global index is returne < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code matrix with bandwidth bw. depending on the value of uplo, a stores either u or l in the equ get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code matrix. depending on the value of uplo, a stores either u or l in the equ get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pcstein does no of orthogonalization is controlled by the input parameter lwork. bal, but the handle (the integer value) may vary array a. the right eigenvector x and the left eigenvector y of t corresponding to an eigenvalue w are defined by t*x = w*x, y'*t = w*y' bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. each row and column of the distributed matrix b with elements b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1 r(i) and c(j) are restricted to be between smlnum = smallest safe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pdgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pdlaebz contains the iteration loop which computes the eigenvalue j = 1,...,minp. it uses and computes the function n(w), which is this is a scalapack internal procedure and arguments are not checked for unreasonable values arguments pdlaed0 computes all eigenvalues and corresponding eigenvectors of the eigenvectors of the original matrix are stored in q, and the eigenvalues are in d. the algorithm consists of three stages the first stage consists of deflating the size of the problem pdlaed2 sorts the two sets of eigenvalues together into a singl there are two ways in which deflation can occur: when two or more pdlaed3 finds the roots of the secular equation, as defined by the values in d, w, and rho, between 1 and k. it makes th where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes find a value for rot cmach (global input) character*1 specifies the value to be returned by pdlamch = 's' or 's , pdlamch := sfmin pdlange returns the value of the one norm, or the frobenius norm distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1). pdlapdct counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. pdlassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code matrix with bandwidth bw. depending on the value of uplo, a stores either u or l in the equ get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. pdstebz computes the eigenvalues of a symmetric tridiagonal matrix i the interval [vl, vu], or the eigenvalues indexed il through iu. a ======= pdstedc computes all eigenvalues and eigenvectors of conquer algorithm. in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pdstein does no of orthogonalization is controlled by the input parameter lwork. pdsyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pdsyevd computes all the eigenvalues and eigenvector of scalapack routines. pdsyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pdsygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form bal, but the handle (the integer value) may vary array a. the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pdzsum1 returns the sum of absolute values of a comple ispec (global input) integer specifies the parameter to be returned as the value o = 1: the data layout blocksize; pscsum1 returns the sum of absolute values of a comple < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. each row and column of the distributed matrix b with elements b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1 r(i) and c(j) are restricted to be between smlnum = smallest safe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. psgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pslaebz contains the iteration loop which computes the eigenvalue j = 1,...,minp. it uses and computes the function n(w), which is this is a scalapack internal procedure and arguments are not checked for unreasonable values arguments pslaed0 computes all eigenvalues and corresponding eigenvectors of the eigenvectors of the original matrix are stored in q, and the eigenvalues are in d. the algorithm consists of three stages the first stage consists of deflating the size of the problem pslaed2 sorts the two sets of eigenvalues together into a singl there are two ways in which deflation can occur: when two or more pslaed3 finds the roots of the secular equation, as defined by the values in d, w, and rho, between 1 and k. it makes th where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes find a value for rot cmach (global input) character*1 specifies the value to be returned by pslamch = 's' or 's , pslamch := sfmin pslange returns the value of the one norm, or the frobenius norm distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1). pslapdct counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. pslassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code matrix with bandwidth bw. depending on the value of uplo, a stores either u or l in the equ get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. psstebz computes the eigenvalues of a symmetric tridiagonal matrix i the interval [vl, vu], or the eigenvalues indexed il through iu. a ======= psstedc computes all eigenvalues and eigenvectors of conquer algorithm. in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. psstein does no of orthogonalization is controlled by the input parameter lwork. pssyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pssyevd computes all the eigenvalues and eigenvector of scalapack routines. pssyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pssygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form bal, but the handle (the integer value) may vary array a. the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. each row and column of the distributed matrix b with elements b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1 r(i) and c(j) are restricted to be between smlnum = smallest safe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pzgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pzheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pzheevd computes all the eigenvalues and eigenvectors of a hermitia pzheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pzhegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form bal, but the handle (the integer value) may vary array a. the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes find a value for rot pzlange returns the value of the one norm, or the frobenius norm distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1). bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pzlassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. scale x so that its components are less than or equal to bignum in absolute value bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. pzmax1 computes the global index of the maximum element in absolute value of a distributed vector sub( x ). the global index is returne < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code matrix with bandwidth bw. depending on the value of uplo, a stores either u or l in the equ get values out of descriptor for use in code bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. < 0: if the i-th argument is an array and the j-entry had an illegal value, then info = -(i*100+j), if the i-t info = -i. get values out of descriptor for use in code matrix. depending on the value of uplo, a stores either u or l in the equ get values out of descriptor for use in code in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pzstein does no of orthogonalization is controlled by the input parameter lwork. bal, but the handle (the integer value) may vary array a. the right eigenvector x and the left eigenvector y of t corresponding to an eigenvalue w are defined by t*x = w*x, y'*t = w*y' bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== zcombamax1 finds the element having maximum real part absolute value as well as its corresponding globl index arguments = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value has been completed, but the factor u is exactly = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== = 0: successful exit < 0: if info = -i, the i-th argument had an illegal value ===================================================================== |
| values values its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pcheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pcheevd computes all the eigenvalues and eigenvectors of a hermitia pcheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pchegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pclassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pcstein does no of orthogonalization is controlled by the input parameter lwork. its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdlabad takes as input the values computed by pdlamch for underflo the log of large is sufficiently large. this subroutine is intended its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdlaebz contains the iteration loop which computes the eigenvalues j = 1,...,minp. it uses and computes the function n(w), which is this is a scalapack internal procedure and arguments are not checked for unreasonable values arguments pdlaed2 sorts the two sets of eigenvalues together into a singl there are two ways in which deflation can occur: when two or more pdlaed3 finds the roots of the secular equation, as defined by the values in d, w, and rho, between 1 and k. it makes th where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdlapdct counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdlassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdstebz computes the eigenvalues of a symmetric tridiagonal matrix i the interval [vl, vu], or the eigenvalues indexed il through iu. a in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pdstein does no of orthogonalization is controlled by the input parameter lwork. pdsyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pdsyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdsygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdzsum1 returns the sum of absolute values of a comple on each processor and hence pjlaenv can return different values on different processors (i.e. local output) further details pscsum1 returns the sum of absolute values of a comple its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pslabad takes as input the values computed by pslamch for underflo the log of large is sufficiently large. this subroutine is intended its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pslaebz contains the iteration loop which computes the eigenvalues j = 1,...,minp. it uses and computes the function n(w), which is this is a scalapack internal procedure and arguments are not checked for unreasonable values arguments pslaed2 sorts the two sets of eigenvalues together into a singl there are two ways in which deflation can occur: when two or more pslaed3 finds the roots of the secular equation, as defined by the values in d, w, and rho, between 1 and k. it makes th where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pslapdct counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pslassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), psstebz computes the eigenvalues of a symmetric tridiagonal matrix i the interval [vl, vu], or the eigenvalues indexed il through iu. a in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. psstein does no of orthogonalization is controlled by the input parameter lwork. pssyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pssyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pssygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pzheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pzheevd computes all the eigenvalues and eigenvectors of a hermitia pzheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pzhegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), the tailored codes place no restrictions on ia, ja, mb or nb. at present, ia, ja, mb and nb are restricted to those values allowe documented below. (search for "restrictions".) its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pzlassq returns the values scl and smsq such tha ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq, its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), get values out of descriptor for use in code in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pzstein does no of orthogonalization is controlled by the input parameter lwork. its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), |
| Variable Variable diag(r) * a(ia:ia+n-1,ja:ja+n-1) * diag(c). equed is an input Variable if fact = 'f'; otherwise, it is a lwork (local input) integer see below for definitions of Variables used to define lwork lwork >= max( nb*( np0+1 ), 3 ) +3*n size of rwork see below for definitions of Variables used to define lrwork lrwork >= 5 * nn + 4 * n size of rwork see below for definitions of Variables used to define lrwork lrwork >= 5 * nn + 4 * n diag(sr) * a * diag(sc). equed is an input Variable if fact = 'f'; otherwise, it is a diag(r) * a(ia:ia+n-1,ja:ja+n-1) * diag(c). equed is an input Variable if fact = 'f'; otherwise, it is a diag(sr) * a * diag(sc). equed is an input Variable if fact = 'f'; otherwise, it is a lwork (local input) integer see below for definitions of Variables used to define lwork lwork >= 5*n + sizesytrd + 1 size of work see below for definitions of Variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) lwork (local input) integer see below for definitions of Variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) diag(r) * a(ia:ia+n-1,ja:ja+n-1) * diag(c). equed is an input Variable if fact = 'f'; otherwise, it is a diag(sr) * a * diag(sc). equed is an input Variable if fact = 'f'; otherwise, it is a lwork (local input) integer see below for definitions of Variables used to define lwork lwork >= 5*n + sizesytrd + 1 size of work see below for definitions of Variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) lwork (local input) integer see below for definitions of Variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) diag(r) * a(ia:ia+n-1,ja:ja+n-1) * diag(c). equed is an input Variable if fact = 'f'; otherwise, it is a lwork (local input) integer see below for definitions of Variables used to define lwork lwork >= max( nb*( np0+1 ), 3 ) +3*n size of rwork see below for definitions of Variables used to define lrwork lrwork >= 5 * nn + 4 * n size of rwork see below for definitions of Variables used to define lrwork lrwork >= 5 * nn + 4 * n diag(sr) * a * diag(sc). equed is an input Variable if fact = 'f'; otherwise, it is a |
| variables variables lwork (local input) integer see below for definitions of variables used to define lwork lwork >= max( nb*( np0+1 ), 3 ) +3*n size of rwork see below for definitions of variables used to define lrwork lrwork >= 5 * nn + 4 * n size of rwork see below for definitions of variables used to define lrwork lrwork >= 5 * nn + 4 * n local variables lwork (local input) integer see below for definitions of variables used to define lwork lwork >= 5*n + sizesytrd + 1 size of work see below for definitions of variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) lwork (local input) integer see below for definitions of variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) local variables lwork (local input) integer see below for definitions of variables used to define lwork lwork >= 5*n + sizesytrd + 1 size of work see below for definitions of variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) lwork (local input) integer see below for definitions of variables used to define lwork lwork >= 5 * n + max( 5 * nn, nb * ( np0 + 1 ) ) local variables lwork (local input) integer see below for definitions of variables used to define lwork lwork >= max( nb*( np0+1 ), 3 ) +3*n size of rwork see below for definitions of variables used to define lrwork lrwork >= 5 * nn + 4 * n size of rwork see below for definitions of variables used to define lrwork lrwork >= 5 * nn + 4 * n local variables |
| variety variety the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, the best algorithm for solving banded and tridiagonal linear systems depends on a variety of parameters, especially the bandwidth implemented. these go by many names, including divide and conquer, |
| various various if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. if the factorization routine and the solve routine are to be called separately (to solve various sets of righthand sides using the sam between calls to the factorization routine and the solve routine. |
| vary vary bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary matrix a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary matrix a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary matrix a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary matrix a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. |
| VCOPY VCOPY the else part of this if needs updated VCOPY, thi the else part of this if needs updated VCOPY, thi |
| VECS VECS if .true., then apply several reflectors at once and read their data from the VECS array t1, t2, and t3. if .true., then apply several reflectors at once and read their data from the VECS array t1, t2, and t3. if .true., then apply several reflectors at once and read their data from the VECS array t1, t2, and t3. if .true., then apply several reflectors at once and read their data from the VECS array t1, t2, and t3. |
| VECT VECT m-by-n matrix a, optionally computing the left and/or right singular VECTors. the svd is written a a = u * sigma * transpose(v) if VECT = 'q', pcunmbr overwrites the general complex distribute m-by-n matrix a, optionally computing the left and/or right singular VECTors. the svd is written a a = u * sigma * transpose(v) if VECT = 'q', pdormbr overwrites the general real distributed m-by- m-by-n matrix a, optionally computing the left and/or right singular VECTors. the svd is written a a = u * sigma * transpose(v) if VECT = 'q', psormbr overwrites the general real distributed m-by- m-by-n matrix a, optionally computing the left and/or right singular VECTors. the svd is written a a = u * sigma * transpose(v) if VECT = 'q', pzunmbr overwrites the general complex distribute |
| vector vector the first iteration of this loop determines a reflection g from the vector v and applies it from left and right to h ctrmvt performs the matrix-vector operation x := conjg( t' ) *y, and w := t *z, compute eigenvectors of matrix blocks dtrmvt performs the matrix-vector operation x := t' *y, and w := t *z, each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. dl (local input/local output) complex pointer to local part of global vector storing the lower diagonal of th aligned with d. dl (local input/local output) complex pointer to local part of global vector storing the lower diagonal of th aligned with d. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pcheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pcheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pchegvx computes all the eigenvalues, and optionally, the eigenvector sub( a )*x=(lambda)*sub( b )*x, sub( a )*sub( b )x=(lambda)*x, or each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis process and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pclacgv conjugates a complex vector of length n, sub( x ), wher x(ix:ix+n-1,jx) if incx = 1, and pclacon estimates the 1-norm of a square, complex distributed matrix a. reverse communication is used for evaluating matrix-vector information is implicitly contained within iv, ix, descv, and descx. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pclaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. where tau is a complex scalar, and v is a complex vector wit a(ia+i+k:ia+n-1,ja+i-1), and tau in tau(ja+i-1). pclamr1d redistributes a one-dimensional row vector from one dat each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector should be aligned with the distributed matrix a. fo process column and replicated over all process rows. similarly, sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes transpose row vector each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. transpose row vector where alpha is a real scalar, and sub( x ) is an (n-1)-element complex distributed vector x(ix:ix+n-2,jx) if incx = 1 an if storev = 'c', the vector which defines the elementary reflecto transpose row vector v (icoffv = iroffc2 each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. transpose row vector v (icoffv = iroffc2 if storev = 'c', the vector which defines the elementary reflecto each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. the routine makes only one pass through the vector sub( x ) notes each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. compute a bound on the computed solution vector to see if th each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pcmax1 computes the global index of the maximum element in absolute value of a distributed vector sub( x ). the global index is returne each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. d (local input/local output) complex pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the since there is no element-by-element vector multiplication i d (local input/local output) complex pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the pcsrscl multiplies an n-element complex distributed vector underflow as long as the final sub( x )/a does not overflow or pcstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pcstein does not each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pctrevc computes some or all of the right and/or left eigenvectors o each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. dl (local input/local output) double precision pointer to local part of global vector storing the lower diagonal of th aligned with d. dl (local input/local output) double precision pointer to local part of global vector storing the lower diagonal of th aligned with d. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pdlacon estimates the 1-norm of a square, real distributed matrix a. reverse communication is used for evaluating matrix-vector products is implicitly contained within iv, ix, descv, and descx. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. where z = q'u, u is a vector of length n with ones in th eigenvalues are close together or if there is a tiny entry in the z vector. for each such occurrence the order of the related secula q (input/output) double precision array, dimension (ldq, n) on entry, q contains the eigenvectors of two submatrices i and (n1+1, n1+1), (n,n). pdlaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. where tau is a real scalar, and v is a real vector wit a(ia+i+k:ia+n-1,ja+i-1), and tau in tau(ja+i-1). pdlamr1d redistributes a one-dimensional row vector from one dat each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector should be aligned with the distributed matrix a. fo process column and replicated over all process rows. similarly, sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. transpose row vector each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. where alpha is a scalar, and sub( x ) is an (n-1)-element real distributed vector x(ix:ix+n-2,jx) if incx = 1 and x(ix,jx:jx+n-2) i if storev = 'c', the vector which defines the elementary reflecto transpose row vector v (icoffv = iroffc2 each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. if storev = 'c', the vector which defines the elementary reflecto each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. the routine makes only one pass through the vector sub( x ) notes each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. d (local input/local output) double precision pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the since there is no element-by-element vector multiplication i d (local input/local output) double precision pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the pdrscl multiplies an n-element real distributed vector sub( x ) b long as the final result sub( x )/a does not overflow or underflow. pdstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pdstein does not pdsyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pdsyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pdsygvx computes all the eigenvalues, and optionally, the eigenvector sub( a )*x=(lambda)*sub( b )*x, sub( a )*sub( b )x=(lambda)*x, or each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis process and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pdzsum1 returns the sum of absolute values of a complex distributed vector sub( x ) in asum where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, pscsum1 returns the sum of absolute values of a complex distributed vector sub( x ) in asum where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. dl (local input/local output) real pointer to local part of global vector storing the lower diagonal of th aligned with d. dl (local input/local output) real pointer to local part of global vector storing the lower diagonal of th aligned with d. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pslacon estimates the 1-norm of a square, real distributed matrix a. reverse communication is used for evaluating matrix-vector products is implicitly contained within iv, ix, descv, and descx. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. where z = q'u, u is a vector of length n with ones in th eigenvalues are close together or if there is a tiny entry in the z vector. for each such occurrence the order of the related secula q (input/output) real array, dimension (ldq, n) on entry, q contains the eigenvectors of two submatrices i and (n1+1, n1+1), (n,n). pslaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. where tau is a real scalar, and v is a real vector wit a(ia+i+k:ia+n-1,ja+i-1), and tau in tau(ja+i-1). pslamr1d redistributes a one-dimensional row vector from one dat each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector should be aligned with the distributed matrix a. fo process column and replicated over all process rows. similarly, sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. transpose row vector each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. where alpha is a scalar, and sub( x ) is an (n-1)-element real distributed vector x(ix:ix+n-2,jx) if incx = 1 and x(ix,jx:jx+n-2) i if storev = 'c', the vector which defines the elementary reflecto transpose row vector v (icoffv = iroffc2 each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. if storev = 'c', the vector which defines the elementary reflecto each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. the routine makes only one pass through the vector sub( x ) notes each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. d (local input/local output) real pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the since there is no element-by-element vector multiplication i d (local input/local output) real pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the psrscl multiplies an n-element real distributed vector sub( x ) b long as the final result sub( x )/a does not overflow or underflow. psstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. psstein does not pssyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pssyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pssygvx computes all the eigenvalues, and optionally, the eigenvector sub( a )*x=(lambda)*sub( b )*x, sub( a )*sub( b )x=(lambda)*x, or each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis process and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pzdrscl multiplies an n-element complex distributed vector underflow as long as the final sub( x )/a does not overflow or dl (local input/local output) complex*16 pointer to local part of global vector storing the lower diagonal of th aligned with d. dl (local input/local output) complex*16 pointer to local part of global vector storing the lower diagonal of th aligned with d. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pzheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pzheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pzhegvx computes all the eigenvalues, and optionally, the eigenvector sub( a )*x=(lambda)*sub( b )*x, sub( a )*sub( b )x=(lambda)*x, or each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis process and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pzlacgv conjugates a complex vector of length n, sub( x ), wher x(ix:ix+n-1,jx) if incx = 1, and pzlacon estimates the 1-norm of a square, complex distributed matrix a. reverse communication is used for evaluating matrix-vector information is implicitly contained within iv, ix, descv, and descx. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pzlaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. where tau is a complex scalar, and v is a complex vector wit a(ia+i+k:ia+n-1,ja+i-1), and tau in tau(ja+i-1). pzlamr1d redistributes a one-dimensional row vector from one dat each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector should be aligned with the distributed matrix a. fo process column and replicated over all process rows. similarly, sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes transpose row vector each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. transpose row vector where alpha is a real scalar, and sub( x ) is an (n-1)-element complex distributed vector x(ix:ix+n-2,jx) if incx = 1 an if storev = 'c', the vector which defines the elementary reflecto transpose row vector v (icoffv = iroffc2 each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. transpose row vector v (icoffv = iroffc2 if storev = 'c', the vector which defines the elementary reflecto each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. the routine makes only one pass through the vector sub( x ) notes each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. compute a bound on the computed solution vector to see if th each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pzmax1 computes the global index of the maximum element in absolute value of a distributed vector sub( x ). the global index is returne each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. d (local input/local output) complex*16 pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the since there is no element-by-element vector multiplication i d (local input/local output) complex*16 pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the pzstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pzstein does not each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. pztrevc computes some or all of the right and/or left eigenvectors o each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. each global data object is described by an associated description vector. this vector stores the information required to establis and memory location. compute eigenvectors of matrix blocks strmvt performs the matrix-vector operation x := t' *y, and w := t *z, the first iteration of this loop determines a reflection g from the vector v and applies it from left and right to h ztrmvt performs the matrix-vector operation x := conjg( t' ) *y, and w := t *z, |
| vectors vectors however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for where tauq and taup are complex scalars, and v and u are complex vectors a(ia+i:ia+m-1,ja+i-1); where tauq and taup are complex scalars, and v and u are complex vectors a(ia+i:ia+m-1,ja+i-1); and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs the local pieces of the distributed matrix solution sub( x ). on exit, the improved solution vectors ix (global input) integer m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) pcheevx computes selected eigenvalues and, optionally, eigenvectors of scalapack routines. eigenvalues/vectors can be selected by where tauq and taup are complex scalars, and v and u are complex vectors if m >= n, v(1:i-1) = 0, v(i) = 1, and v(i:m) is stored on exit in because vectors may be viewed as a subclass of matrices, n (global input) integer the length of the distributed vectors v and x. n >= 0 v (local workspace) complex pointer into the local pclaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. the elements of the vectors v together form the (n-k+1)-by-nb matri unreduced part of the matrix, using an update of the form: sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise because vectors may be viewed as a subclass of matrices, storev (global input) character*1 specifies how the vectors which define the elementar = 'c': columnwise storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise (not supported yet) storev (global input) character specifies how the vectors which define the elementar = 'c': columnwise (not supported yet) because vectors may be viewed as a subclass of matrices, the elements of the vectors v together form the n-by-nb matrix part of the matrix, using a hermitian rank-2k update of the form: because vectors may be viewed as a subclass of matrices, on entry, this array contains the the local pieces of the solution vectors sub( x ). on exit, it contains th however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for because vectors may be seen as particular matrices, a distribute pcstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pcstein does not pctrevc computes some or all of the right and/or left eigenvectors o on entry, this array contains the the local pieces of the solution vectors sub( x ) ix (global input) integer vect='q', and (lld_a,locc(ja+nq-1)) if vect = 'p'. nq = m if side = 'l', and nq = n otherwise. the vectors whic products determine the matrices q and p, as returned by to an array of dimension (lld_a,locc(ja+m-1)) if side='l', and (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic to an array of dimension (lld_a,locc(ja+m-1)) if side='l', or (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic if side = 'l', lld_a >= max(1,locr(ia+m-1)); however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for where tauq and taup are real scalars, and v and u are real vectors a(ia+i:ia+m-1,ja+i-1); where tauq and taup are real scalars, and v and u are real vectors a(ia+i:ia+m-1,ja+i-1); and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs the local pieces of the distributed matrix solution sub( x ). on exit, the improved solution vectors ix (global input) integer m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) where tauq and taup are real scalars, and v and u are real vectors if m >= n, v(1:i-1) = 0, v(i) = 1, and v(i:m) is stored on exit in n (global input) integer the length of the distributed vectors v and x. n >= 0 v (local workspace) double precision pointer into the local pdlaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. the elements of the vectors v together form the (n-k+1)-by-nb matri unreduced part of the matrix, using an update of the form: sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise because vectors may be viewed as a subclass of matrices, storev (global input) character*1 specifies how the vectors which define the elementar = 'c': columnwise storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise (not supported yet) storev (global input) character specifies how the vectors which define the elementar = 'c': columnwise (not supported yet) pdlasrt sort the numbers in d in increasing order and the corresponding vectors in q arguments because vectors may be viewed as a subclass of matrices, the elements of the vectors v together form the n-by-nb matrix part of the matrix, using a symmetric rank-2k update of the form: vect='q', and (lld_a,locc(ja+nq-1)) if vect = 'p'. nq = m if side = 'l', and nq = n otherwise. the vectors whic products determine the matrices q and p, as returned by to an array of dimension (lld_a,locc(ja+m-1)) if side='l', and (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic to an array of dimension (lld_a,locc(ja+m-1)) if side='l', or (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic if side = 'l', lld_a >= max(1,locr(ia+m-1)); on entry, this array contains the the local pieces of the solution vectors sub( x ). on exit, it contains th however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for because vectors may be seen as particular matrices, a distribute pdstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pdstein does not pdsyevx computes selected eigenvalues and, optionally, eigenvectors of scalapack routines. eigenvalues/vectors can be selected by on entry, this array contains the the local pieces of the solution vectors sub( x ) ix (global input) integer because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for where tauq and taup are real scalars, and v and u are real vectors a(ia+i:ia+m-1,ja+i-1); where tauq and taup are real scalars, and v and u are real vectors a(ia+i:ia+m-1,ja+i-1); and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs the local pieces of the distributed matrix solution sub( x ). on exit, the improved solution vectors ix (global input) integer m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) where tauq and taup are real scalars, and v and u are real vectors if m >= n, v(1:i-1) = 0, v(i) = 1, and v(i:m) is stored on exit in n (global input) integer the length of the distributed vectors v and x. n >= 0 v (local workspace) real pointer into the local pslaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. the elements of the vectors v together form the (n-k+1)-by-nb matri unreduced part of the matrix, using an update of the form: sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise because vectors may be viewed as a subclass of matrices, storev (global input) character*1 specifies how the vectors which define the elementar = 'c': columnwise storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise (not supported yet) storev (global input) character specifies how the vectors which define the elementar = 'c': columnwise (not supported yet) pslasrt sort the numbers in d in increasing order and the corresponding vectors in q arguments because vectors may be viewed as a subclass of matrices, the elements of the vectors v together form the n-by-nb matrix part of the matrix, using a symmetric rank-2k update of the form: vect='q', and (lld_a,locc(ja+nq-1)) if vect = 'p'. nq = m if side = 'l', and nq = n otherwise. the vectors whic products determine the matrices q and p, as returned by to an array of dimension (lld_a,locc(ja+m-1)) if side='l', and (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic to an array of dimension (lld_a,locc(ja+m-1)) if side='l', or (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic if side = 'l', lld_a >= max(1,locr(ia+m-1)); on entry, this array contains the the local pieces of the solution vectors sub( x ). on exit, it contains th however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for because vectors may be seen as particular matrices, a distribute psstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. psstein does not pssyevx computes selected eigenvalues and, optionally, eigenvectors of scalapack routines. eigenvalues/vectors can be selected by on entry, this array contains the the local pieces of the solution vectors sub( x ) ix (global input) integer because vectors may be seen as particular matrices, a distribute however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for where tauq and taup are complex scalars, and v and u are complex vectors a(ia+i:ia+m-1,ja+i-1); where tauq and taup are complex scalars, and v and u are complex vectors a(ia+i:ia+m-1,ja+i-1); and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs the local pieces of the distributed matrix solution sub( x ). on exit, the improved solution vectors ix (global input) integer m-by-n matrix a, optionally computing the left and/or right singular vectors. the svd is written a a = u * sigma * transpose(v) pzheevx computes selected eigenvalues and, optionally, eigenvectors of scalapack routines. eigenvalues/vectors can be selected by where tauq and taup are complex scalars, and v and u are complex vectors if m >= n, v(1:i-1) = 0, v(i) = 1, and v(i:m) is stored on exit in because vectors may be viewed as a subclass of matrices, n (global input) integer the length of the distributed vectors v and x. n >= 0 v (local workspace) complex*16 pointer into the local pzlaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. the elements of the vectors v together form the (n-k+1)-by-nb matri unreduced part of the matrix, using an update of the form: sub( a ) = a(ia:ia+m-1,ja:ja+n-1) using the row and scaling factors in the vectors r and c notes sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the scaling factors in the vectors sr and sc notes storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise because vectors may be viewed as a subclass of matrices, storev (global input) character*1 specifies how the vectors which define the elementar = 'c': columnwise storev (global input) character indicates how the vectors which define the elementar = 'c': columnwise (not supported yet) storev (global input) character specifies how the vectors which define the elementar = 'c': columnwise (not supported yet) because vectors may be viewed as a subclass of matrices, the elements of the vectors v together form the n-by-nb matrix part of the matrix, using a hermitian rank-2k update of the form: because vectors may be viewed as a subclass of matrices, on entry, this array contains the the local pieces of the solution vectors sub( x ). on exit, it contains th however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for however, for tridiagonal matrices, since the objects being distributed are the individual vectors storing the diagonals, w the 1-by-p descriptor are allowed and are equivalent for pzstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pzstein does not pztrevc computes some or all of the right and/or left eigenvectors o on entry, this array contains the the local pieces of the solution vectors sub( x ) ix (global input) integer vect='q', and (lld_a,locc(ja+nq-1)) if vect = 'p'. nq = m if side = 'l', and nq = n otherwise. the vectors whic products determine the matrices q and p, as returned by to an array of dimension (lld_a,locc(ja+m-1)) if side='l', and (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic to an array of dimension (lld_a,locc(ja+m-1)) if side='l', or (lld_a,locc(ja+n-1)) if side = 'r'. the vectors whic if side = 'l', lld_a >= max(1,locr(ia+m-1)); |
| verify verify the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices sub( a ), sub( z ) must verify should be true: the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*), c(ic:ic+m-1,jc:jc+n-1), and b( ib:ib+n-1, jb:jb+n-1 ) must verify some alignment properties the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin n-by-nrhs distributed matrix denoted by sub( b ). a check is made to verify that sub( a ) is nonsingular notes the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and z(iz:iz+m-1,jz:jz+n-1) must verify some alignment properties, namely the followin the distributed submatrices sub( a ), sub( z ) must verify should be true: the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*), c(ic:ic+m-1,jc:jc+n-1), and b( ib:ib+n-1, jb:jb+n-1 ) must verify some alignment properties the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with n-by-nrhs distributed matrix denoted by sub( b ). a check is made to verify that sub( a ) is nonsingular notes the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and z(iz:iz+m-1,jz:jz+n-1) must verify some alignment properties, namely the followin the distributed submatrices sub( a ), sub( z ) must verify should be true: the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*), c(ic:ic+m-1,jc:jc+n-1), and b( ib:ib+n-1, jb:jb+n-1 ) must verify some alignment properties the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with n-by-nrhs distributed matrix denoted by sub( b ). a check is made to verify that sub( a ) is nonsingular notes the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices sub( a ) and sub( b ) must verify som the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices sub( a ), sub( z ) must verify should be true: the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*), c(ic:ic+m-1,jc:jc+n-1), and b( ib:ib+n-1, jb:jb+n-1 ) must verify some alignment properties the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with the distributed submatrix sub( a ) must verify some alignment proper ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin n-by-nrhs distributed matrix denoted by sub( b ). a check is made to verify that sub( a ) is nonsingular notes the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin the distributed submatrices a(ia:*, ja:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin |
| versa versa pclacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes pdlacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes pslacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes pzlacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes |
| version version this is the unblocked version of the algorithm, calling level 2 blas arguments this is the unblocked version of the algorithm, calling level 2 blas arguments one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 this is the right-looking parallel level 2 blas version of th this is the right-looking parallel level 3 blas version of th version 1.4 limitations desca(m_) = descz(m_) version 1.4 limitations desca(m_) = descz(m_) pchentrd is a prototype version of pchetrd which uses tailore when the workspace provided by the user is adequate. the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the serial version clacon has been contributed by nick higham march 16, 1988. the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the serial version was contributed to lapack by nick higham for us one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 all of b or a submatrix of b). important note: the current version of this code support the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x ===================================================================== one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 this is the right-looking parallel level 2 blas version of th this is the right-looking parallel level 3 blas version of th the serial version dlacon has been contributed by nick higham march 16, 1988. the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 all of b or a submatrix of b). important note: the current version of this code support the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x ===================================================================== dimension (lwork) version 1.0: on output, work(1) returns the workspac if the input parameters are incorrect, work(1) may also be version 1.4 limitations desca(m_) = descz(m_) pdsyntrd is a prototype version of pdsytrd which uses tailore when the workspace provided by the user is adequate. the serial version of this routine was originally contributed b this version provides a set of parameters which should give good computers. users are encouraged to modify this subroutine to set the serial version of this routine was originally contributed b one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 this is the right-looking parallel level 2 blas version of th this is the right-looking parallel level 3 blas version of th the serial version slacon has been contributed by nick higham march 16, 1988. the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 all of b or a submatrix of b). important note: the current version of this code support the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x ===================================================================== dimension (lwork) version 1.0: on output, work(1) returns the workspac if the input parameters are incorrect, work(1) may also be version 1.4 limitations desca(m_) = descz(m_) pssyntrd is a prototype version of pssytrd which uses tailore when the workspace provided by the user is adequate. one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x ===================================================================== one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 this is the right-looking parallel level 2 blas version of th this is the right-looking parallel level 3 blas version of th version 1.4 limitations desca(m_) = descz(m_) version 1.4 limitations desca(m_) = descz(m_) pzhentrd is a prototype version of pzhetrd which uses tailore when the workspace provided by the user is adequate. the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the serial version zlacon has been contributed by nick higham march 16, 1988. the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and m_x the serial version was contributed to lapack by nick higham for us one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 one-dimensional descriptors are a new addition to scalapack since version 1.0. they simplify and shorten the descriptor for 1 all of b or a submatrix of b). important note: the current version of this code support this is the unblocked version of the algorithm, calling level 2 blas arguments this is the unblocked version of the algorithm, calling level 2 blas arguments |
| versions versions arithmetic, this needs to be larger. the default for publicly released versions should be large enough to handl on the accuracy of the solution. arithmetic, this needs to be larger. the default for publicly released versions should be large enough to handl on the accuracy of the solution. |
| very very non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature scale factors for sub( a ). r is aligned with the distributed matrix a, and replicated across every process column. r i convergence of a double shift if their product is small relatively even if each is not very small. thus it i the lapack algorithm zlahqr, a loop of m goes from i-2 down to everyone needs to receive the new nbulg non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature for sub( a ). sr is aligned with the distributed matrix a, and replicated across every process column. sr is tied to th non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature scale factors for sub( a ). r is aligned with the distributed matrix a, and replicated across every process column. r i convergence of a double shift if their product is small relatively even if each is not very small. thus it i the lapack algorithm dlahqr, a loop of m goes from i-2 down to this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits when we hit a border, there are row and column transforms that overlap over several processors and the code gets very *local* matrix is generated on one node (called smalla) and non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature for sub( a ). sr is aligned with the distributed matrix a, and replicated across every process column. sr is tied to th non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature scale factors for sub( a ). r is aligned with the distributed matrix a, and replicated across every process column. r i convergence of a double shift if their product is small relatively even if each is not very small. thus it i the lapack algorithm dlahqr, a loop of m goes from i-2 down to this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits when we hit a border, there are row and column transforms that overlap over several processors and the code gets very *local* matrix is generated on one node (called smalla) and non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature for sub( a ). sr is aligned with the distributed matrix a, and replicated across every process column. sr is tied to th non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature scale factors for sub( a ). r is aligned with the distributed matrix a, and replicated across every process column. r i convergence of a double shift if their product is small relatively even if each is not very small. thus it i the lapack algorithm zlahqr, a loop of m goes from i-2 down to everyone needs to receive the new nbulg non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature for sub( a ). sr is aligned with the distributed matrix a, and replicated across every process column. sr is tied to th non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature |
| via via its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a cal locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a cal locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), eigenvalues. this is done by finding the roots of the secular equation via the routine slaed4 (as called by pdlaed3) problem. its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), most parameters set via a call to pjlaenv must be identica value to all procesors (i.e. global output). however some, its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a cal locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), eigenvalues. this is done by finding the roots of the secular equation via the routine slaed4 (as called by pslaed3) problem. its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a cal locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), |
| viewed viewed because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, |
| vise vise pclacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes pdlacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes pslacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes pzlacp3 is an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa. notice tha more. the receiving node can be specified precisely, or all nodes |
| vol vol a real or complex matrix, with applications to condition estimation", acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== a real or complex matrix, with applications to condition estimation", acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== a real or complex matrix, with applications to condition estimation", acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== a real or complex matrix, with applications to condition estimation", acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== |