On Augmented Lagrangian Methods for Saddle-Point Linear Systems with Singular or Semidefinite (1, 1) Blocks
Year: 2014
Journal of Computational Mathematics, Vol. 32 (2014), Iss. 3 : pp. 297–305
Abstract
An effective algorithm for solving large saddle-point linear systems, presented by Krukier et al., is applied to the constrained optimization problems. This method is a modification of skew-Hermitian triangular splitting iteration methods. We consider the saddle-point linear systems with singular or semidefinite (1, 1) blocks. Moreover, this method is applied to precondition the GMRES. Numerical results have confirmed the effectiveness of the method and showed that the new method can produce high-quality preconditioners for the Krylov subspace methods for solving large sparse saddle-point linear systems.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1401-CR7
Journal of Computational Mathematics, Vol. 32 (2014), Iss. 3 : pp. 297–305
Published online: 2014-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 9
Keywords: Hermitian and skew-Hermitian splitting Saddle-point linear system Constrained optimization Krylov subspace method.
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The Parameterized Augmentation Block Preconditioner for Nonsymmetric Saddle Point Problems
Wu, Bo
(2024)
https://doi.org/10.1007/s42967-024-00383-0 [Citations: 0]