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Lecture
Linear Optimization: Auxiliary Problem
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Related lectures (32)
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Network Flows and LP Formulations
Explains network flows, LP formulations, simplex method, duality, and practical applications.
Lagrangian Duality: Convex Optimization
Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.
Dual Translations in Linear Programming
Explores dual translations in linear programming, emphasizing primal and dual formulations and the significance of invertible submatrices.
Linear Programming Duality
Explores the concept of duality in linear programming and its practical implications in optimization.
Optimization in Networks
Explores duality in network optimization problems with examples and step-by-step explanations.
Semi-Definite Programming
Covers semi-definite programming and optimization over positive semidefinite cones.
Primal-dual Optimization: Fundamentals
Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.
Lagrangian Duality: Theory and Applications
Explores Lagrangian duality in convex optimization, discussing strong duality, dual solutions, and practical applications in second-order cone programs.
Solving Linear Programs: SIMPLEX Method
Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.
Weak and Strong Duality
Covers weak and strong duality in optimization problems, focusing on Lagrange multipliers and KKT conditions.
