An Improved Wavelet Based Preconditioner for Sparse Linear Problems | Zendy

A. Padmanabha Reddy | Zendy; Nagendrapp M. Bujurke | Zendy

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An Improved Wavelet Based Preconditioner for Sparse Linear Problems

Author(s) -

A. Padmanabha Reddy,

Nagendrapp M. Bujurke

Publication year - 2010

Publication title -

applied mathematics

Language(s) - English

Resource type - Journals

eISSN - 2152-7393

pISSN - 2152-7385

DOI - 10.4236/am.2010.15049

Subject(s) - preconditioner , generalized minimal residual method , krylov subspace , wavelet , linear system , mathematics , sparse matrix , computer science , matrix (chemical analysis) , discrete wavelet transform , algorithm , iterative method , wavelet transform , mathematical optimization , artificial intelligence , mathematical analysis , physics , materials science , quantum mechanics , composite material , gaussian

In this paper, we present the construction of purely algebraic Daubechies wavelet based preconditioners for Krylov subspace iterative methods to solve linear sparse system of equations. Effective preconditioners are designed with DWTPerMod algorithm by knowing size of the matrix and the order of Daubechies wavelet. A notable feature of this algorithm is that it enables wavelet level to be chosen automatically making it more robust than other wavelet based preconditioners and avoids user choosing a level of transform. We demonstrate the efficiency of these preconditioners by applying them to several matrices from Tim Davis collection of sparse matrices for restarted GMRES

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