Krylov subspace methods - Theory, algorithms, and applicationsProjection methods based on Krylov subspaces for solving various types of scientific problems are reviewed. The main idea of this class of methods when applied to a linear system Ax = b, is to generate in some manner an approximate solution to the original problem from the so-called Krylov subspace span. Thus, the original problem of size N is approximated by one of dimension m, typically much smaller than N. Krylov subspace methods have been very successful in solving linear systems and eigenvalue problems and are now becoming popular for solving nonlinear equations. The main ideas in Krylov subspace methods are shown and their use in solving linear systems, eigenvalue problems, parabolic partial differential equations, Liapunov matrix equations, and nonlinear system of equations are discussed.
Document ID
19920043589
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Sad, Youcef (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1990
Subject Category
Numerical Analysis
Meeting Information
Meeting: International Conference on the Computing Methods in Applied Sciences and Engineering