Constraint Tightening

Constraint tightening involves modifying constraints in optimization problems to improve computational efficiency or robustness, often by sacrificing some optimality. Current research focuses on developing methods for tightening constraints in various contexts, including neural network verification, stochastic control, and trajectory planning, employing techniques like mixed-integer programming, regression models, and convexification. These advancements are crucial for tackling computationally challenging problems in diverse fields, enabling more efficient and reliable solutions in areas such as power system analysis, robotics, and machine learning. The ultimate goal is to find the optimal balance between constraint tightness and computational tractability, leading to more practical and effective algorithms.