Chance Constrained Optimization

Chance-constrained optimization (CCO) tackles optimization problems where constraints might be violated due to uncertainty, aiming to find solutions that satisfy these constraints with a specified probability. Current research focuses on developing efficient algorithms, such as those based on conformal prediction, Monte Carlo approximation, and mixed-integer programming, to solve CCO problems across diverse applications. These advancements are improving the robustness and reliability of solutions in various fields, including robotics, resource allocation, and policy learning, by explicitly managing risk under uncertainty. The resulting methodologies offer significant improvements over deterministic approaches, particularly in safety-critical systems.