Generates the orthogonal matrix Q of the RQ factorization formed by p?gerqf.
This section describes ScaLAPACK routines for solving nonsymmetric eigenvalue problems, computing the Schur factorization of general matrices, as well as performing a number of related computational tasks.
To solve a nonsymmetric eigenvalue problem with ScaLAPACK, you usually need to reduce the matrix to the upper Hessenberg form and then solve the eigenvalue problem with the Hessenberg matrix obtained.
Computes the solution to the system of linear equations with a general banded distributed matrix and multiple right-hand sides.
Computes the singular value decomposition of a general matrix, optionally computing the left and/or right singular vectors.
Balances a general real matrix.
Exploits IEEE arithmetic to accelerate the computations of eigenvalues. (C interface function).
Redistributes an array assuming that the input array byrow is distributed across columns and that all process rows contain the same copy of byrow.
Reduces the first nb rows and columns of a symmetric/Hermitian matrix A to real tridiagonal form by an orthogonal/unitary similarity transformation.
Computes the Cholesky factorization of a symmetric/Hermitian positive definite matrix (local unblocked algorithm).
Finds a new relatively robust representation such that at least one of the eigenvalues is relatively isolated.