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On the regularization matrix of the regularized DPSS preconditioner for non-Hermitian saddle-point problems

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Abstract

In this work, with respect to the regularization matrix of the new regularized deteriorated positive and skew-Hermitian splitting (RDPSS) preconditioner for non-Hermitian saddle-point problems, we provide a class of Hermitian positive semidefinite matrices, which depend on certain parameters, for practical computations. A precise description about the eigenvalue distribution of the corresponding preconditioned matrix is given. The condition number of the eigenvector matrix, which partly determines the convergence rate of the related preconditioned Krylov subspace method, is also discussed in this work. Finally, some numerical experiments are carried out to identify the effectiveness of the presented special choices for the regularization matrix to solve the non-Hermitian saddle-point problems.

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Authors and Affiliations

  1. School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, 201620, People’s Republic of China

    Ju-Li Zhang

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  1. Ju-Li Zhang
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Correspondence to Ju-Li Zhang.

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Communicated by Jinyun Yuan.

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The work is partially supported by National Natural Science Foundation of China (No. 11801362) and (No. 11601323).

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Zhang, JL. On the regularization matrix of the regularized DPSS preconditioner for non-Hermitian saddle-point problems. Comp. Appl. Math. 39, 191 (2020). https://doi.org/10.1007/s40314-020-01226-3

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  • DOI: https://doi.org/10.1007/s40314-020-01226-3

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