Hyuk Park – Optimization Online

Differentiating Through Moving Recourse: Feasible Policy Optimization and Finite-Sample Certification for Multistage Stochastic Programs

Published: 2026/09/27

Dynamic Programming, Energy, Stochastic Programming differentiable projection, information relaxation, moving recourse, multistage stochastic programming, optimality gap certification, pathwise gradient, policy optimization, reservoir management

Multistage stochastic programs model decisions under uncertainty where earlier decisions change later feasible sets. We propose a feasible pathwise gradient method (FeasPG) that can update the policy as new sample paths arrive. The policy proposes a decision and projects it onto the current feasible set, so every decision is feasible. We derive the full trajectory … Read more

Data-Driven Stochastic Dual Dynamic Programming: Performance Guarantees and Regularization Schemes

Published: 2022/12/20, Updated: 2025/05/19

Stochastic Programming distributionally robust optimization, Markov dependence, stochastic dual dynamic programming, stochastic optimization

We propose a data-driven extension of the stochastic dual dynamic programming (SDDP) algorithm for multistage stochastic linear programs under a continuous-state, non-stationary Markov data process. Unlike traditional SDDP methods—which often assume a known probability distribution, stagewise independent data process, or uncertainty restricted to the right-hand side of constraints—our approach overcomes these limitations, making it more … Read more