A generalized structured doubling algorithm for optimal control problems
Volker Mehrmann, Federico Poloni · 2010
We propose a generalization of the Structured Doubling Algorithm (SDA) to compute invariant subspaces of structured matrix pencils that arise in the context of solving linear quadratic optimal control problems. The new algorithm is designed to attain better accuracy when the classical Riccati equation approach for the solution of the optimal control problem is not well suited because the stable and unstable invariant subspaces are not well separated (due to eigenvalues near or on the imaginary axis) or in the case when the Riccati solution does not exist at all. We analyze the convergence of the method and compare the new method with the classical SDA algorithm as well as some recent structured QR-methods.