A New Iterative Method for Solving Large-Scale Lyapunov Matrix Equations 论文

2007SIAM Journal on Scientific Computing引用 345
Matrix Theory and AlgorithmsNumerical methods for differential equationsModel Reduction and Neural Networks

摘要

In this paper we propose a new projection method to solve large-scale continuous-time Lyapunov matrix equations. The new approach projects the problem onto a much smaller approximation space, generated as a combination of Krylov subspaces in A and $A^{-1}$. The reduced problem is then solved by means of a direct Lyapunov scheme based on matrix factorizations. The reported numerical results show the competitiveness of the new method, compared to a state-of-the-art approach based on the factorized alternating direction implicit iteration.