Abstract
This article continues the research on combined stochastic-deterministic iterative algorithms for solving large system of linear algebraic equations we developed in our previous study [K. K. Sabelfeld and G. Agarkov, Randomized vector algorithm with iterative refinement for solving boundary integral equations, Monte Carlo Methods Appl. 30 2024, 4, 375–388]. In this paper we focus on two issues: Variance reduction and extension of randomized algorithms by combining them with Krylov type iterative methods like the method of conjugate gradients, the conjugate residual method, and Craig’s method. The developed randomized algorithms are applied to boundary integral equations for 2D and 3D Laplace equations.
Funding source: Russian Science Foundation
Award Identifier / Grant number: 24-11-00107
Funding statement: Support by the Russian Science Foundation under Grant 24-11-00107 is greatly acknowledged.
References
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Articles in the same Issue
- Frontmatter
- Impact of psychrometry on the aerosol distribution pattern in human lungs
- Bayesian inference of traffic intensity in M/M/1 queue under symmetric and asymmetric loss functions
- On the variance of Schatten p-norm estimation with Gaussian sketching matrices
- A meshfree Random Walk on Boundary algorithm with iterative refinement
- Combining randomized and deterministic iterative algorithms for high accuracy solution of large linear systems and boundary integral equations
- Preservation of structural properties of the CIR model by θ-Milstein schemes