Parallel solutions of large Riccati equations using epsilon decomposition

We present a new parallel algorithm which is designed to accurately solve large, sparse Riccati equations. The algorithm utilizes epsilon decompositions to obtain a good initial approximation for the solution, and subsequently combines the Arnoldi Krylov subspace method with Newton's iterative process to refine this approximation. Experimental results are presented for three large electric power systems, illustrating that in a multiprocessor environment this approach can produce accurate solutions with only modest demands on the execution time and memory.