Local Optimality

Local optimality, a central concept in optimization and machine learning, focuses on finding solutions that are optimal within a limited neighborhood, rather than globally. Current research investigates its implications across diverse areas, including reinforcement learning (e.g., using variance-reduced policy iteration), gradient-based optimization (exploring dynamic stepsize scheduling and the impact of local minima/saddle points), and submodular function minimization. Understanding and mitigating the limitations of local optima is crucial for improving the efficiency and accuracy of algorithms in various applications, from training neural networks to solving complex combinatorial problems.