B. V. R. Kumar and M. Mehra, “Wavelet-Based Preconditioners for Sparse Linear Systems,” Applied Mathematics and Computation, Vol. 171, No. 1, 1 December 2005, pp. 203-224.

has been cited by the following article:

• TITLE: An Improved Wavelet Based Preconditioner for Sparse Linear Problems

  AUTHORS: Arikera Padmanabha Reddy, Nagendrapp M. Bujurke

  KEYWORDS: Discrete Wavelet Transform, Preconditioners, Sparse Matrices, Krylov Subspace Iterative Methods

  JOURNAL NAME: Applied Mathematics, Vol.1 No.5, November 30, 2010

  ABSTRACT: 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.