Constrained Convex Optimization: Min-Max Formulation and Fenchel Conjugation

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Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Equivalent formulation

Covers the concept of equivalent formulation in constrained optimization and explores the tangent cone.

Optimization Methods: Theory Discussion

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

Mathematics of Data: Optimization Basics

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Fenchel Conjugation: Basics and Applications

Introduces Fenchel conjugation, exploring its properties, examples, and applications in nonsmooth optimization problems and minimax formulations.

Optimal Control: Unconstrained and Constrained Problems

Explores optimal control in unconstrained and constrained problems, emphasizing the importance of sparsity.

Optimization with Constraints: KKT Conditions

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Lagrange Multipliers Theorem

Explores the Lagrange multipliers theorem for n>2, demonstrating its application in optimization problems with constraints.