Randomized vector iterative linear solvers of high precision for large dense system

Sabelfeld Karl K. () and Kireeva Anastasiya ()
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Sabelfeld Karl K.: Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Science, Novosibirsk, Russia
Kireeva Anastasiya: Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Science, Novosibirsk, Russia

Monte Carlo Methods and Applications, 2023, vol. 29, issue 4, 323-332

Abstract: In this paper we suggest randomized linear solvers with a focus on refinement issue to achieve a high precision while maintaining all the advantages of the Monte Carlo method for solving systems of large dimension with dense matrices. It is shown that each iterative refinement step reduces the error by one order of magnitude. The crucial point of the suggested method is, in contrast to the standard Monte Carlo method, that the randomized vector algorithm computes the entire solution column at once, rather than a single component. This makes it possible to efficiently construct the iterative refinement method. We apply the developed method for solving a system of elasticity equations.

Keywords: Randomized algorithm; matrix vector multiplication; large system of linear equations; matrix iterations; iterative refinement; randomized conjugate residual method (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/mcma-2023-2013

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