On finding robust approximate inverses for large sparse matrices

DOI10.1080/03081087.2013.825910zbMath1317.65081OpenAlexW1968844996MaRDI QIDQ2929473

Fazlollah Soleymani

Publication date: 12 November 2014

Published in: Linear and Multilinear Algebra (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03081087.2013.825910

zbMATH Keywords

algorithmconvergencenumerical examplesparallel computationerror estimatepreconditionerMoore-Penrose inverserectangular matrixgeneralized minimal residual (GMRES) methodapproximate matrix inversionlarge sparse complex matricesSchulz iterations

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Iterative numerical methods for linear systems (65F10) Parallel numerical computation (65Y05) Direct numerical methods for linear systems and matrix inversion (65F05) Preconditioners for iterative methods (65F08)

Related Items (18)

Exploiting higher computational efficiency index for computing outer generalized inverses ⋮ An efficient hyperpower iterative method for computing weighted Moore-Penrose inverse ⋮ Construction of a convergent scheme for finding matrix sign function ⋮ An efficient matrix iteration family for finding the generalized outer inverse ⋮ Computing outer inverses by scaled matrix iterations ⋮ An efficient matrix iterative method for computing Moore-Penrose inverse ⋮ Finding generalized inverses by a fast and efficient numerical method ⋮ On hyperpower family of iterations for computing outer inverses possessing high efficiencies ⋮ An efficient computation of generalized inverse of a matrix ⋮ A fast and efficient Newton-Shultz-type iterative method for computing inverse and Moore-Penrose inverse of tensors ⋮ A rapid and powerful iterative method for computing inverses of sparse tensors with applications ⋮ Factorizations of hyperpower family of iterative methods via least squares approach ⋮ A survey on the high convergence orders and computational convergence orders of sequences ⋮ A class of Kung-Traub-type iterative algorithms for matrix inversion ⋮ Further efficient hyperpower iterative methods for the computation of generalized inverses \(A_{T,S}^{(2)}\) ⋮ Hyperpower least squares progressive iterative approximation ⋮ Finding the Moore-Penrose inverse by a new matrix iteration ⋮ Constructing two-step iterative methods with and without memory

Uses Software

• Mathematica

Cites Work

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