A Generic Solver for Unconstrained Control Problems with Integral Functional Objectives

Abstract

We present a generic solver for unconstrained control problems (UCPs) whose objectives take the form of an integral functional of the controllers. The solver generalizes and improves upon the algorithm proposed by Tseng and Tang for the Witsenhausen's counterexample, which provides the best-known results. In essence, we show that minimizing the objective implies minimizing the marginal cost functions almost everywhere, and we perform the latter task pointwisely by the adaptive minimization technique, which speeds up the computation. We implement single-threaded and parallelized versions of the proposed algorithm. Our implementation runs $30 \times$ faster than Tseng and Tang's algorithm on the Witsenhausen's counterexample, and we demonstrate the applicability of the solver and discuss the possible generalization to constrained problems through two more examples.