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)
Journal	Journal of Computational and Applied Mathematics
Volume	412
DOIs	https://doi.org/10.1016/j.cam.2022.114298
State	Published - Oct 1 2022
Externally published	Yes

ASJC Scopus subject areas

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",

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year = "2022",

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

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T1 - Block triangular preconditioning for stochastic Galerkin method

AU - Wang, Dongwu

AU - Zheng, Bin

AU - Chen, Long

AU - Lin, Guang

AU - Xu, Jinchao

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

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.

UR - https://linkinghub.elsevier.com/retrieve/pii/S0377042722001248

UR - http://www.scopus.com/inward/record.url?scp=85128791445&partnerID=8YFLogxK

U2 - 10.1016/j.cam.2022.114298

DO - 10.1016/j.cam.2022.114298

M3 - Article

SN - 0377-0427

VL - 412

JO - Journal of Computational and Applied Mathematics

JF - Journal of Computational and Applied Mathematics

ER -

Block triangular preconditioning for stochastic Galerkin method

Abstract

ASJC Scopus subject areas

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