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Iterative Algorithms for Symmetric Positive Semidefinite Solutions of the Lyapunov Matrix Equations

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  • Min Sun
  • Jing Liu

Abstract

It is well-known that the stability of a first-order autonomous system can be determined by testing the symmetric positive definite solutions of associated Lyapunov matrix equations. However, the research on the constrained solutions of the Lyapunov matrix equations is quite few. In this paper, we present three iterative algorithms for symmetric positive semidefinite solutions of the Lyapunov matrix equations. The first and second iterative algorithms are based on the relaxed proximal point algorithm (RPPA) and the Peaceman–Rachford splitting method (PRSM), respectively, and their global convergence can be ensured by corresponding results in the literature. The third iterative algorithm is based on the famous alternating direction method of multipliers (ADMM), and its convergence is subsequently discussed in detail. Finally, numerical simulation results illustrate the effectiveness of the proposed iterative algorithms.

Suggested Citation

  • Min Sun & Jing Liu, 2020. "Iterative Algorithms for Symmetric Positive Semidefinite Solutions of the Lyapunov Matrix Equations," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, February.
    • Handle: RePEc:hin:jnlmpe:6968402
    DOI: 10.1155/2020/6968402
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