A global random walk on grid algorithm for second order elliptic equations

Sabelfeld Karl K. (), Smirnov Dmitry (), Dimov Ivan () and Todorov Venelin ()
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Sabelfeld Karl K.: Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Sciences, Novosibirsk, Russia
Smirnov Dmitry: Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Sciences, Novosibirsk, Russia
Dimov Ivan: Department of Parallel Algorithms, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Acad. G. Bonchev Str., Block 25 A, 1113, Sofia, Bulgaria
Todorov Venelin: Department of Information Modeling, Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Acad. Georgi Bonchev Str., Block 8, 1113; and Department of Parallel Algorithms, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Acad. G. Bonchev Str., Block 25 A, 1113, Sofia, Bulgaria

Monte Carlo Methods and Applications, 2021, vol. 27, issue 4, 325-339

Abstract: In this paper we develop stochastic simulation methods for solving large systems of linear equations, and focus on two issues: (1) construction of global random walk algorithms (GRW), in particular, for solving systems of elliptic equations on a grid, and (2) development of local stochastic algorithms based on transforms to balanced transition matrix. The GRW method calculates the solution in any desired family of prescribed points of the gird in contrast to the classical stochastic differential equation based Feynman–Kac formula. The use in local random walk methods of balanced transition matrices considerably decreases the variance of the random estimators and hence decreases the computational cost in comparison with the conventional random walk on grids algorithms.

Keywords: Stochastic algorithms; large linear systems; global random walk on grid; balanced sampling; Monte Carlo methods (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/mcma-2021-2097

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