Iterative least-squares solutions of coupled sylvester matrix equations. (English) Zbl 1129.65306

Ding, Feng; Chen, Tongwen

Syst. Control Lett. 54, No. 2, 95-107 (2005).

Summary: We present a general family of iterative methods to solve linear equations, which includes the well-known Jacobi and Gauss–Seidel iterations as its special cases. The methods are extended to solve coupled Sylvester matrix equations. In our approach, we regard the unknown matrices to be solved as the system parameters to be identified, and propose a least-squares iterative algorithm by applying a hierarchical identification principle and by introducing the block-matrix inner product (the star product for short). We prove that the iterative solution consistently converges to the exact solution for any initial value. The algorithms proposed require less storage capacity than the existing numerical ones. Finally, the algorithms are tested on computer and the results verify the theoretical findings.

MSC:

65F10	Iterative numerical methods for linear systems
93B40	Computational methods in systems theory (MSC2010)
93E10	Estimation and detection in stochastic control theory

Keywords:

sylvester matrix equation; Lyapunov matrix equation; identification; estimation; least squares; Jacobi iteration; Gauss–Seidel iteration; Hadamard product; star product; hierarchical identification principle

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