Optimal rank matrix algebras preconditioners
Abstract
When a linear system Ax = y is solved by means of iterative methods (mainly CG and GMRES) and the convergence rate is slow, one may consider a preconditioner P. The use of such preconditioner changes the spectrum of the matrix defining the system and could result into a great acceleration of the convergence rate. The construction of optimal rank preconditioners is strongly related to the possibility of splitting A as A = P + R + E, where E is a small perturbation and R is of low rank. In the present work we extend the black-dot algorithm for the computation of such splitting for P circulant, to the case where P is in L, for several known low-complexity matrix algebras L. The algorithm so obtained is particularly efficient when A is Toeplitz plus Hankel like. We finally discuss in detail the existence and the properties of the decomposition A = P + R + E when A is Toeplitz, also extending to the phi-circulant and Hartley-type cases some results previously known for P circulant.
Cite
@article{arxiv.1304.0563,
title = {Optimal rank matrix algebras preconditioners},
author = {F. Tudisco and C. Di Fiore and E. E. Tyrtyshnikov},
journal= {arXiv preprint arXiv:1304.0563},
year = {2013}
}