Uncertainty Set

Uncertainty sets represent the range of possible values for uncertain parameters in optimization and decision-making problems, aiming to find solutions robust to this uncertainty. Current research focuses on developing efficient algorithms for computing robust solutions within various uncertainty set structures (e.g., ellipsoids, zonotopes, $L_1$ and $L_\infty$ balls), often employing techniques from robust optimization, machine learning (e.g., policy gradient methods, neural networks), and game theory. This work is significant for improving the reliability and performance of algorithms across diverse fields, including portfolio optimization, control systems, and reinforcement learning, by explicitly accounting for uncertainty in model parameters and data.