Global Optimality

Global optimality in optimization problems seeks to find the absolute best solution, rather than merely a good enough one, a crucial aspect for reliable and efficient applications. Current research focuses on developing algorithms with provable convergence guarantees to global optima, addressing challenges posed by non-convexity and high dimensionality in various contexts, including bilevel optimization, Bayesian networks, reinforcement learning, and neural networks. These advancements are significant because achieving global optimality enhances the trustworthiness and performance of machine learning models and optimization techniques across diverse fields like robotics, molecular design, and control systems.