Safe Optimization

Safe optimization focuses on finding optimal solutions while guaranteeing safety constraints are never violated, a crucial aspect in various applications like robotics and industrial control. Current research emphasizes developing efficient algorithms, such as Bayesian optimization and evolutionary strategies (like CMA-ES), often incorporating Gaussian processes for uncertainty modeling and handling time-varying constraints. These advancements improve the reliability and performance of optimization in high-stakes scenarios where unsafe solutions are unacceptable, impacting fields ranging from autonomous systems to process control.