Efficient Solution of Sequences of Parametrized Lyapunov Equations With Applications

Abstract

Sequences of parametrized Lyapunov equations can be encountered in many application settings. Moreover, solutions of such equations are often intermediate steps of an overall procedure whose main goal is the computation of , where denotes the solution of a Lyapunov equation and is a given matrix. We are interested in addressing problems where the parameter dependency of the coefficient matrix is encoded as a low‐rank modification to a seed, fixed matrix. We propose two novel numerical procedures that fully exploit such a common structure. The first one builds upon the Sherman‐Morrison‐Woodbury (SMW) formula and recycling Krylov techniques, and it is well‐suited for small dimensional problems as it makes use of dense numerical linear algebra tools. The second algorithm can instead address large‐scale problems by relying on state‐of‐the‐art projection techniques based on the extended Krylov subspace. We test the new algorithms on several problems arising in the study of damped vibrational systems and the analyses of output synchronization problems for multi‐agent systems. Our results show that the algorithms we propose are superior to state‐of‐the‐art techniques as they are able to remarkably speed up the computation of accurate solutions.