An efficient method for solving non-negative Sylvester matrix equations

Lijun Xu; Ting Li; Yijia Zhou

doi:10.1117/12.2672442

28 March 2023 An efficient method for solving non-negative Sylvester matrix equations

Lijun Xu, Ting Li, Yijia Zhou

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125970J (2023) https://doi.org/10.1117/12.2672442
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Alternating Direction Method of Multipliers (ADMM) is an effective method for solving separable convex optimization problems. In this paper, the method is extended to solve Sylvester equations with nonnegative constraint. We give a convergence result showing that the algorithm converges to a Karush-Kuhn-Tucker point whenever it converges. Numerical evidence shows that the proposed algorithm can efficiently solve nonnegative Sylvester equations.

Citation Download Citation

Lijun Xu, Ting Li, and Yijia Zhou "An efficient method for solving non-negative Sylvester matrix equations", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125970J (28 March 2023); https://doi.org/10.1117/12.2672442

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