Contents

# Symmetric Eigenvalue Problems: LAPACK Driver Routines

This
topic
describes LAPACK driver routines used for solving symmetric eigenvalue problems. See also computational routines that can be called to solve these problems. Table
"Driver Routines for Solving Symmetric Eigenproblems"
lists all such driver routines.
Driver Routines for Solving Symmetric Eigenproblems
Routine Name
Operation performed
Computes all eigenvalues and, optionally, eigenvectors of a real symmetric / Hermitian matrix.
Computes all eigenvalues and (optionally) all eigenvectors of a real symmetric / Hermitian matrix using divide and conquer algorithm.
Computes selected eigenvalues and, optionally, eigenvectors of a symmetric / Hermitian matrix.
Computes selected eigenvalues and, optionally, eigenvectors of a real symmetric / Hermitian matrix using the Relatively Robust Representations.
Computes all eigenvalues and, optionally, eigenvectors of a real symmetric / Hermitian matrix in packed storage.
Uses divide and conquer algorithm to compute all eigenvalues and (optionally) all eigenvectors of a real symmetric / Hermitian matrix held in packed storage.
Computes selected eigenvalues and, optionally, eigenvectors of a real symmetric / Hermitian matrix in packed storage.
Computes all eigenvalues and, optionally, eigenvectors of a real symmetric / Hermitian band matrix.
Computes all eigenvalues and (optionally) all eigenvectors of a real symmetric / Hermitian band matrix using divide and conquer algorithm.
Computes selected eigenvalues and, optionally, eigenvectors of a real symmetric / Hermitian band matrix.
Computes all eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix.
Computes all eigenvalues and (optionally) all eigenvectors of a real symmetric tridiagonal matrix using divide and conquer algorithm.
Computes selected eigenvalues and eigenvectors of a real symmetric tridiagonal matrix.
Computes selected eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix using the Relatively Robust Representations.

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