Primal-dual Optimization: Fundamentals

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

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

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

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

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

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

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

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