Lanran Fang – 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

Distributionally Robust Optimization via Targeted Integral Probability Metrics for General Data Processes

Published: 2026/05/28, Updated: 2026/07/05

Optimization in Data Science, Robust Optimization, Stochastic Programming distributionally robust optimization, heavy-tailed distributions, incomplete data, integral probability metrics, markovian data, outlier-corrupted data, semi-infinite programming

Distributionally robust optimization (DRO) provides a principled framework for decision-making under distributional uncertainty. Classical data-driven DRO frameworks typically construct ambiguity sets from distributional information, such as moment constraints, divergence neighborhoods, or Wasserstein balls, specified before the downstream loss is considered. We propose a task-aware DRO framework based on targeted integral probability metrics. The ambiguity set … Read more