  In this work, we propose an algorithm to price American options by directly
solving the dual minimization problem introduced by Rogers. Our approach relies
on approximating the set of uniformly square integrable martingales by a finite
dimensional Wiener chaos expansion. Then, we use a sample average approximation
technique to efficiently solve the optimization problem. Unlike all the
regression based methods, our method can transparently deal with path dependent
options without extra computations and a parallel implementation writes easily
with very little communication and no centralized work. We test our approach on
several multi--dimensional options with up to 40 assets and show the impressive
scalability of the parallel implementation.
