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. © 2007 Society for Industrial and Applied Mathematics.