Chance Constrained

Chance-constrained optimization tackles optimization problems where constraints involve uncertain parameters, aiming to find solutions that satisfy these constraints with a specified probability. Current research focuses on developing efficient algorithms, such as model predictive control and evolutionary algorithms, to handle various chance constraint formulations, including distributionally robust approaches that account for ambiguity in the underlying probability distributions. This field is crucial for applications requiring robust decision-making under uncertainty, impacting areas like robotics, autonomous systems, and fair machine learning, where safety and reliability are paramount.