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 selected eigenvalues and, optionally, eigenvectors of a symmetric matrix.
Computes all eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix.
Computes the SVD of a bidiagonal matrix.
Computes selected eigenvalues and, optionally, eigenvectors of a generalized Hermitian positive-definite eigenproblem with matrices in packed storage.
Multiplies a complex matrix by a square real matrix.
Returns the value of the 1-norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a trapezoidal or triangular matrix.