Block triangular preconditioning for stochastic Galerkin method

Dongwu Wang; Bin Zheng; Long Chen; Guang Lin; Jinchao Xu

doi:10.1016/j.cam.2022.114298

Block triangular preconditioning for stochastic Galerkin method

Dongwu Wang, Bin Zheng, Long Chen, Guang Lin, Jinchao Xu

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we develop a new block triangular preconditioner for solving partial differential equations with random coefficients. We prove spectral bounds for the preconditioned system. Several numerical examples are provided to demonstrate the efficiency of this preconditioner, especially for stochastic problems with large variance.

Original language	English (US)
Article number	114298
Journal	Journal of Computational and Applied Mathematics
Volume	412
DOIs	https://doi.org/10.1016/j.cam.2022.114298
State	Published - Oct 1 2022

All Science Journal Classification (ASJC) codes

Computational Mathematics
Applied Mathematics

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10.1016/j.cam.2022.114298

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title = "Block triangular preconditioning for stochastic Galerkin method",

abstract = "In this paper, we develop a new block triangular preconditioner for solving partial differential equations with random coefficients. We prove spectral bounds for the preconditioned system. Several numerical examples are provided to demonstrate the efficiency of this preconditioner, especially for stochastic problems with large variance.",

author = "Dongwu Wang and Bin Zheng and Long Chen and Guang Lin and Jinchao Xu",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",

year = "2022",

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doi = "10.1016/j.cam.2022.114298",

language = "English (US)",

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AU - Wang, Dongwu

AU - Zheng, Bin

AU - Chen, Long

AU - Lin, Guang

AU - Xu, Jinchao

PY - 2022/10/1

Y1 - 2022/10/1

N2 - In this paper, we develop a new block triangular preconditioner for solving partial differential equations with random coefficients. We prove spectral bounds for the preconditioned system. Several numerical examples are provided to demonstrate the efficiency of this preconditioner, especially for stochastic problems with large variance.

AB - In this paper, we develop a new block triangular preconditioner for solving partial differential equations with random coefficients. We prove spectral bounds for the preconditioned system. Several numerical examples are provided to demonstrate the efficiency of this preconditioner, especially for stochastic problems with large variance.

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DO - 10.1016/j.cam.2022.114298

M3 - Article

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VL - 412

JO - Journal of Computational and Applied Mathematics

JF - Journal of Computational and Applied Mathematics

M1 - 114298

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Block triangular preconditioning for stochastic Galerkin method

Abstract

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