Algorithm 845: EIGIFP: a MATLAB program for solving large symmetric generalized eigenvalue problems

Authors:
James H. Money;Qiang Ye
Affiliations:
University of Kentucky, Lexington, KY;University of Kentucky, Lexington, KY
Venue:
ACM Transactions on Mathematical Software (TOMS)
Year:
2005

Citing 8
Cited 3

Sparse matrix test problems

ACM Transactions on Mathematical Software (TOMS)
A Jacobi--Davidson Iteration Method for Linear EigenvalueProblems

SIAM Journal on Matrix Analysis and Applications
The symmetric eigenvalue problem

The symmetric eigenvalue problem
Jacobi--Davidson Style QR and QZ Algorithms for the Reduction of Matrix Pencils

SIAM Journal on Scientific Computing
Templates for the solution of algebraic eigenvalue problems: a practical guide

Templates for the solution of algebraic eigenvalue problems: a practical guide
Toward the Optimal Preconditioned Eigensolver: Locally Optimal Block Preconditioned Conjugate Gradient Method

SIAM Journal on Scientific Computing
An Inverse Free Preconditioned Krylov Subspace Method for Symmetric Generalized Eigenvalue Problems

SIAM Journal on Scientific Computing
Algorithm 827: irbleigs: A MATLAB program for computing a few eigenpairs of a large sparse Hermitian matrix

ACM Transactions on Mathematical Software (TOMS)

A block inverse-free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems

Journal of Computational and Applied Mathematics
Lagrangian Duality and Branch-and-Bound Algorithms for Optimal Power Flow

Operations Research
An inexact spectral bundle method for convex quadratic semidefinite programming

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

eigifp is a MATLAB program for computing a few extreme eigenvalues and eigenvectors of the large symmetric generalized eigenvalue problem Ax = λ Bx. It is a black-box implementation of an inverse free preconditioned Krylov subspace projection method developed by Golub and Ye [2002]. It has important features that allow it to solve some difficult problems without any input from users. It is particularly suitable for problems where preconditioning by the standard shift-and-invert transformation is not feasible.