  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.
