Strong Duality

Strong duality, a concept arising in optimization and its applications across diverse fields, focuses on establishing equivalence between primal and dual formulations of problems. Current research explores strong duality in various contexts, including reinforcement learning (using primal-dual algorithms and optimal transport), machine learning (analyzing its role in neural network training and model comparisons), and other areas like information extraction and market making. These investigations aim to improve algorithm efficiency, robustness, and theoretical understanding, leading to advancements in both theoretical computer science and practical applications such as robotics and financial modeling.