Robust Optimization
Overview
It formulates decisions against uncertainty using worst-case objectives, regret, or distributionally robust ambiguity sets, including Wasserstein and scenario-based models. Methods range from convex and dual reformulations and scenario reduction to conformal calibration, learned uncertainty sets, and robust predict-then-optimize. Applications include portfolios, supply chains, scheduling, resource allocation, and machine learning, often evaluated under distribution shifts or adversarial perturbations. A central tension is how to provide useful guarantees without making decisions overly conservative or computationally intractable; calibration, problem structure, and the choice of uncertainty set remain decisive.
Latest papers
49 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.