Successive Linear Approximation Solution of Infinite-Horizon Dynamic Stochastic Programs

Abstract

Models for long-term planning often lead to infinite-horizon stochastic programs that offer significant challenges for computation. Finite-horizon approximations are often used in these cases, but they may also become computationally difficult. In this paper, we directly solve for value functions of infinite-horizon stochastic programs. We show that a successive linear approximation method converges to an optimal value function for the case with convex objective, linear dynamics, and feasible continuation.