Computes a blocked QR factorization of a real or complex "triangular-pentagonal" matrix, which is composed of a triangular block and a pentagonal block, using the compact WY representation for Q.
Reduces a complex Hermitian matrix to tridiagonal form with Successive Bandwidth Reduction approach.
Computes all eigenvalues and eigenvectors of a symmetric or Hermitian matrix reduced to tridiagonal form (QR algorithm).
This topic describes LAPACK routines for solving nonsymmetric eigenvalue problems, computing the Schur factorization of general matrices, as well as performing a number of related computational tasks.
A nonsymmetric eigenvalue problem is as follows: given a nonsymmetric (or non-Hermitian) matrix A, find the eigenvaluesλ and the corresponding eigenvectorsz that satisfy the equation
Reduces a pair of matrices to generalized upper Hessenberg form using orthogonal/unitary transformations.
Computes the CS decomposition of an orthogonal/unitary matrix in bidiagonal-block form.
Computes all eigenvalues and, optionally, all eigenvectors of a complex Hermitian matrix using divide and conquer algorithm.
Computes selected eigenvalues and, optionally, eigenvectors of a Hermitian band matrix.
Computes the SVD and left and right singular vectors for a matrix.
Computes selected eigenvalues and, optionally, eigenvectors of a real generalized symmetric definite eigenproblem with matrices in packed storage.