Convex Optimization: Duality and KKT Conditions

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Convex Optimization Problems: Theory and Applications

Explores convex optimization problems, optimality criteria, equivalent problems, and practical applications in transportation and robotics.

Convex Optimization: Exercises

Covers exercises related to convex optimization, including SOCP dual, KKT conditions, and equality constrained least squares.

Optimization with Constraints: KKT Conditions

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

Convex Optimization Problems: Standard Form

Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.

Legendre Transform

Explores the Legendre transform, duality in convex analysis, and optimization problems.

Optimization Techniques: Convexity and Algorithms in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.

Linear Programming: Weighted Bipartite Matching

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

Weak and Strong Duality

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

Distributionally Robust Portfolio Optimization

Explores distributionally robust portfolio optimization and compares different estimation approaches and methods for portfolio evaluation.

Optimization Techniques: Convexity in Machine Learning

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