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Sparsified Randomization Algorithms for large systems of linear equations and a new version of the Random Walk on Boundary method

Author

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  • Sabelfeld K.

    (Institute of Computational Mathematics and Mathem. Geophysics, Russian Acad. Sci., Lavrentieva str., 6, 630090 Novosibirsk, Russia. Email: karl@osmf.sscc.ru)

  • Mozartova N.

    (Novosibirsk State University, Pirogova str., 2, 630090 Novosibirsk, Russia. Email: nmozartova@mail.ru)

Abstract

Sparsified Randomization Monte Carlo (SRMC) algorithms for solving large systems of linear algebraic equations are presented. We construct efficient stochastic algorithms based on a probabilistic sampling of small size sub-matrices, or a randomized evaluation of a matrix-vector product and matrix iterations via a random sparsification of the matrix. This approach is beyond the standard Markov chain based Neumann–Ulam method which has no universal instrument to decrease the variance. Instead, in the new method, first, the variance can be decreased by increasing the number of the sampled columns of the matrix in play, and second, it is free of the restricted assumption of the Neumann–Ulam scheme that the Neumann series converges. We apply the developed methods to different stochastic iterative procedures. Application to boundary integral equation of the electrostatic potential theory is given where we develop a SRMC algorithm for solving the approximated system of linear algebraic equations, and compare it with the standard Random Walk on Boundary method.

Suggested Citation

  • Sabelfeld K. & Mozartova N., 2009. "Sparsified Randomization Algorithms for large systems of linear equations and a new version of the Random Walk on Boundary method," Monte Carlo Methods and Applications, De Gruyter, vol. 15(3), pages 257-284, January.
    • Handle: RePEc:bpj:mcmeap:v:15:y:2009:i:3:p:257-284:n:5
    DOI: 10.1515/MCMA.2009.015
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    Cited by:

    1. Sabelfeld, K.K. & Mozartova, N.S., 2011. "Sparsified Randomization algorithms for low rank approximations and applications to integral equations and inhomogeneous random field simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(2), pages 295-317.
    2. Sabelfeld, Karl K., 2018. "Stochastic projection methods and applications to some nonlinear inverse problems of phase retrieving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 169-175.
    3. Sabelfeld Karl K., 2016. "Splitting and survival probabilities in stochastic random walk methods and applications," Monte Carlo Methods and Applications, De Gruyter, vol. 22(1), pages 55-72, March.
    4. Sabelfeld Karl K., 2016. "Vector Monte Carlo stochastic matrix-based algorithms for large linear systems," Monte Carlo Methods and Applications, De Gruyter, vol. 22(3), pages 259-264, September.
    5. Sabelfeld, Karl K., 2018. "A random walk on spheres based kinetic Monte Carlo method for simulation of the fluctuation-limited bimolecular reactions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 46-56.
    6. Sabelfeld Karl & Mozartova Nadezhda, 2012. "Stochastic boundary collocation and spectral methods for solving PDEs," Monte Carlo Methods and Applications, De Gruyter, vol. 18(3), pages 217-263, September.
    7. Sabelfeld Karl & Loshchina Nadja, 2010. "Stochastic iterative projection methods for large linear systems," Monte Carlo Methods and Applications, De Gruyter, vol. 16(3-4), pages 343-359, January.
    8. Grigoriu Mircea, 2014. "An efficient Monte Carlo solution for problems with random matrices," Monte Carlo Methods and Applications, De Gruyter, vol. 20(2), pages 121-136, June.

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