Stochastic iterative refinement with preconditioning for solving Helmholtz equation via boundary integral equation
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Karl K. Sabelfeld
and
Alexander Prokopiev
Abstract
This work suggests different Monte Carlo algorithms for solving large systems of linear algebraic equations arising from the numerical solution of the Dirichlet problem for the Helmholtz equation. Approach based on boundary integral representations, vector randomization algorithm, method of fundamental solutions, stochastic projection algorithm, and randomized singular value decomposition are applied. It is shown that the use of stochastic iterative refinement and preconditioning can significantly improve the accuracy and stability of the computations. Simulation results are presented, demonstrating the effectiveness of the proposed methods.
Funding source: Russian Science Foundation
Award Identifier / Grant number: 24-11-00107
Funding statement: Support of the Russian Science Foundation under Grant 24-11-00107 is gratefully acknowledged.
References
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