Primal-dual Optimization: Methods and Applications

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Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.

Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

Explores optimal control and KKT conditions for non-linear optimization with constraints.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Explores primal-dual optimization with a focus on Lagrangian methods and their convergence, drawbacks, and enhancements.

Explores primal-dual optimization algorithms for convex-concave minimax problems, discussing convergence properties and applications.

Introduces linear algebra, calculus, and optimization basics in Euclidean spaces, emphasizing the power of optimization as a modeling tool.