Robust Dual Dynamic Programming

Robust Dual Dynamic Programming

Multi-stage robust optimization problems, where the decision maker can dynamically react to consecutively observed realizations of the uncertain problem parameters, pose formidable theoretical and computational challenges. As a result, the existing solution approaches for this problem class typically determine suboptimal solutions under restrictive assumptions. In this paper, we propose a robus...

Stable Dual Dynamic Programming

Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintaining explicit representations of stationary distributions instead of value functions. In this paper, we investigate the convergence properties of these dual algorithms both theoretically and empirically, and show how they can be scaled up by incorporating function approximation.

Robust dynamic programming via multi-parametric programming

In this work, we present a new algorithm for solving complex multi-stage optimisation problems involving hard constraints and uncertainties, based on dynamic and multi-parametric programming. Each echelon of the dynamic programming procedure, typically employed in the context of multi-stage optimisation models, is interpreted as a robust multi-parametric optimisation problem, with the present s...

Stochastic Dual Dynamic Integer Programming

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty, dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A common formulation for these problems is a dynamic programming formulation involving nested cost-to-go functions. In the linear setting, the cost-to-go functions are convex polyhedral, and decomposition algorithms, suc...

Robust Parsing Using Dynamic Programming