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Anderson Accelerated PMHSS for Complex-Symmetric Linear Systems
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-6384-2630
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). RaySearch Laboratories, RaySearch Laboratories.ORCID iD: 0000-0001-6865-9379
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0003-0639-0639
2024 (English)In: 2024 SIAM Conference on Parallel Processing for Scientific Computing, PP 2024, Society for Industrial and Applied Mathematics Publications , 2024, p. 39-51Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the design and development of an Anderson Accelerated Preconditioned Modified Hermitian and Skew-Hermitian Splitting (AA-PMHSS) method for solving complex-symmetric linear systems with application to electromagnetics problems, such as wave scattering and eddy currents. While it has been shown that the Anderson acceleration of real linear systems is essentially equivalent to GMRES, we show here that the formulation using Anderson acceleration leads to a more performant method. We show relatively good robustness compared to existing preconditioned GMRES methods and significantly better performance due to the faster evaluation of the preconditioner. In particular, AA-PMHSS can be applied to solve problems and equations arising from complex-valued systems, such as time-harmonic eddy current simulations discretized with the Finite Element Method. We also evaluate three test systems present in previous literature. We show that the method is competitive with two types of preconditioned GMRES, which share the significant advantage of having a convergence rate that is independent of the discretization size.

Place, publisher, year, edition, pages
Society for Industrial and Applied Mathematics Publications , 2024. p. 39-51
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-347317ISI: 001282154500004Scopus ID: 2-s2.0-85194149187OAI: oai:DiVA.org:kth-347317DiVA, id: diva2:1867250
Conference
22nd SIAM Conference on Parallel Processing for Scientific Computing, PP 2024, Baltimore, United States of America, Mar 5 2024 - Mar 8 2024
Note

QC 20240612

Part of ISBN 978-171389347-9

Available from: 2024-06-10 Created: 2024-06-10 Last updated: 2024-09-10Bibliographically approved

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Andersson, MånsLiu, FelixMarkidis, Stefano

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CiteExportLink to record
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