Approximate solutions and eigenvalue bounds from Krylov subspaces 论文
1995Numerical Linear Algebra with Applications引用 261
Matrix Theory and AlgorithmsStatistical and numerical algorithmsElectromagnetic Scattering and Analysis
摘要
Abstract Approximations to the solution of a large sparse symmetric system of equations are considered. The conjugate gradient and minimum residual approximations are studied without reference to their computation. Several different bases for the associated Krylov subspace are used, including the usual Lanczos basis. The zeros of the iteration polynomial for the minimum residual approximation ( harmonic Ritz values) are characterized in several ways and, in addition, attractive convergence properties are established. The connection of these harmonic Ritz values to Lehmann's optimal intervals for eigenvalues of the original matrix appears to be new.