An Inner-Outer Iteration Algorithm with Optimal Parameters for Stochastic Lyapunov Matrix Equation
For researchers working on discrete-time stochastic linear systems, this work provides a more efficient algorithm for solving the stochastic Lyapunov matrix equation, though it is an incremental improvement over existing iterative methods.
This paper proposes an inner-outer iterative algorithm with optimal parameters for solving the stochastic Lyapunov matrix equation, establishing convergence conditions and demonstrating through numerical examples that it outperforms existing iterative methods.
This paper proposes an inner--outer (IO) iterative algorithm with optimal parameters for solving stochastic Lyapunov matrix equation associated with discrete-time stochastic linear system. First, under the assumption that the underlying stochastic linear system is asymptotically mean-square stable, the monotonicity and boundedness of the iterative sequence generated by the proposed algorithm are analyzed. On this basis, a sufficient convergence result is established for the zero initial condition. Second, by deriving the spectral radius of the corresponding iteration matrix, several necessary and sufficient convergence conditions are obtained for arbitrary initial conditions. In addition, the optimal parameter-selection strategies are developed to improve the convergence performance of the algorithm. Finally, numerical examples are presented to verify the theoretical results and demonstrate the advantages of the proposed algorithm over several existing iterative methods.