Optimal rank matrix algebras preconditioners

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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 and move to the preconditioned system P- 1Ax=P- 1y. 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 (Tyrtyshnikov, 1996) [1]. In the present work we extend the black-dot algorithm for the computation of such splitting for P circulant (see Oseledets and Tyrtyshnikov, 2006 [2]), to the case where P is in A, for several known low-complexity matrix algebras A. 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 -circulant and Hartley-type cases some results previously known for P circulant. © 2012 Elsevier Inc. All rights reserved.

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Tudisco, F., Di Fiore, C., & Tyrtyshnikov, E. E. (2013). Optimal rank matrix algebras preconditioners. Linear Algebra and Its Applications, 438(1), 405–427. https://doi.org/10.1016/j.laa.2012.07.042

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