Minimax Optimization: Theory and Algorithms

This lecture covers the theory and algorithms of minimax optimization, focusing on weak and strong duality, saddle points, necessary and sufficient conditions for strong duality, Slater's qualification condition, numerical e-accuracy, primal-dual gap functions, optimality conditions, and practical performance of optimization algorithms. The instructor discusses the application of gradient descent-ascent (GDA) and its performance on simple problems, as well as the challenges posed by nonconvex-concave and nonconvex-nonconcave problems.