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Lecture
Duality in Linear Programming
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Related lectures (32)
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Dual Translations in Linear Programming
Explores dual translations in linear programming, emphasizing primal and dual formulations and the significance of invertible submatrices.
Primal-dual optimization: Theory and Computation
Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.
Linear Programming Duality
Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.
Optimization Principles
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Primal-dual Optimization: Fundamentals
Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.
Two-phase Simplex Algorithm: Introduction and Duality
Introduces the two-phase simplex algorithm and explores duality in linear programming.
Linear Programming Basics
Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.
Approximation Algorithms
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Linear Programming: Optimization and Duality
Introduces linear programming, optimization, and duality concepts with a focus on the simplex method and practical examples.
Convex Optimization Problems: Theory and Applications
Explores convex optimization problems, optimality criteria, equivalent problems, and practical applications in transportation and robotics.
