Global Optimum

Finding global optima—the absolute best solutions—in complex, often non-convex, landscapes is a central challenge across numerous scientific and engineering disciplines. Current research focuses on developing efficient algorithms, including Bayesian optimization, evolutionary strategies (like CMA-ES), and deep learning-based approaches, to locate not only single global optima but also multiple optima in multimodal problems, often within dynamic or noisy environments. These advancements are crucial for accelerating scientific discovery (e.g., materials science, drug design) and improving the performance of machine learning models and other high-stakes applications by ensuring reliable and optimal solutions. The development of robust and efficient methods for finding global optima remains an active area of investigation, with a growing emphasis on theoretical guarantees and rigorous benchmarking.