A Computationally Efficient FPTAS for Convex Stochastic Dynamic Programs

A Computationally Efficient FPTAS for Convex Stochastic Dynamic Programs

We propose a computationally efficient Fully Polynomial-Time Approximation Scheme(FPTAS) to compute an approximation with arbitrary precision of the value function of convexstochastic dynamic programs, using the technique of K-approximation sets and functions introducedby Halman et al. (2009). This paper deals with the convex case only, and it has the following con-tributions: F...

A Fast FPTAS for Convex Stochastic Dynamic Programs

We propose an efficient Fully Polynomial-Time Approximation Scheme (FPTAS) for stochastic convex dynamic programs using the technique of K-approximation sets and functions introduced by Halman et al. This paper deals with the convex case only, and it has the following contributions: First, we improve on the worst-case running time given by Halman et al. Second, we design an FPTAS with excellent...

Information Relaxations, Duality, and Convex Stochastic Dynamic Programs (Online Appendix)

Information Relaxations, Duality, and Convex Stochastic Dynamic Programs

We consider the information relaxation approach for calculating performance bounds for stochastic dynamic programs (DPs). This approach generates performance bounds by solving problems with relaxed nonanticipativity constraints and a penalty that punishes violations of these nonanticipativity constraints. In this paper, we study DPs that have a convex structure and consider gradient penalties t...

Hierarchical Sparsity in Multistage Convex Stochastic Programs