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Lecture
Linear Programming Duality
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Related lectures (36)
Optimal Decision Analysis
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Explores strong duality, complementary slackness, economic interpretation, and stochastic problem scenarios in linear programming.
Dual Translations in Linear Programming
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Explores dual translations in linear programming, emphasizing primal and dual formulations and the significance of invertible submatrices.
Linear Programming: Two-phase Simplex Algorithm
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Covers the application of the two-phase Simplex algorithm to solve linear programming problems.
Linear Programming Basics
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Covers the basics of linear programming, defining corners, extreme points, and feasible solutions within polyhedrons.
Certificates: Existence and Proving
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Explores certificates, proving solution existence, constraints, and linear programming duality.
Optimization Principles
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Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Lagrangian Duality: Theory and Applications
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Explores Lagrangian duality in convex optimization, discussing strong duality, dual solutions, and practical applications in second-order cone programs.
Optimization in Networks
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Explores duality in network optimization problems with examples and step-by-step explanations.
Relations Between Events
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Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.
Proof of Strong Duality
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Covers the proof of strong duality in optimization problems and provides examples of Rayleigh quotient optimization.
Linear Programming Basics
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Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.
Cutset Formulation: MST Problem
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Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.
Simplex Algorithm: Exercises & Interpretation
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Covers exercises on the Simplex Algorithm, optimizing solutions subject to linear constraints.
LP Duality: An Economics Perspective
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Explores LP duality in zero-sum games from an economics perspective, emphasizing optimal strategies and decision-making.
Projected Gradient Descent and Quadratic Penalty
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Covers Projected Gradient Descent and Quadratic Penalty methods for optimization problems.
Nonparametric Regression: Kernel-Based Estimation
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Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.
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