Primal-dual optimization: Theory and Computation

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

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

Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.

Covers optimality conditions, strong duality, and complementarity slackness in linear optimization.

Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.

Explores minimax optimization theory, including weak and strong duality, saddle points, and practical algorithm performance.

Covers weak and strong duality in optimization problems, focusing on Lagrange multipliers and KKT conditions.

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.

Covers the concept of linear optimization and the duality relationship between primal and dual problems.

Explores the formulation of the auxiliary problem in linear optimization and its role in optimal decision-making.