Local Optimization

Local optimization focuses on finding improved solutions within a limited search space, aiming to efficiently enhance existing solutions rather than exploring the entire problem domain. Current research emphasizes developing and analyzing local optimization algorithms within various contexts, including neural networks for topology optimization and federated learning, as well as exploring novel approaches like geometrically-inspired kernel machines and Bayesian optimization strategies. These advancements are significant for improving the efficiency and scalability of optimization in diverse fields, ranging from automated driving systems and distributed AI to solving complex problems in operations research and engineering.