Factorization of saddle-point matrices in dynamical systems optimization—reusing pivots

Abstract

In this paper we consider the application of direct methods for solving a sequence of saddle-point systems. Our goal is to design a method that reuses information from one factorization and applies it to the next one. In more detail, when we compute the pivoted LDLT factorization we speed up computation by reusing already computed pivots and permutations. We develop our method in the frame of dynamical systems optimization. Experiments show that the method improves efficiency over Bunch–Parlett and Bunch–Kaufman while delivering the same results.