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MATH-261: Discrete optimization
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Lectures in this course (65)
Dijkstra's Algorithm Overview
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Demonstrates the iterative process of applying Dijkstra's algorithm to find optimal paths.
Linear Programming Duality
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Explores the concept of duality in linear programming and its practical implications in optimization.
Convex Polyhedra and Linear Programs
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Explores convex polyhedra, linear programs, and their optimization importance.
Linear Inequalities and Equivalent LP
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Covers linear inequalities, active constraints, and equivalent linear programs.
Solving Linear Programs: SIMPLEX Method
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Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.
Linear Programming Basics
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Covers the basics of linear programming and the simplex method, focusing on finding optimal solutions and handling degeneracy.
Certificates: Existence and Proving
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Explores certificates, proving solution existence, constraints, and linear programming duality.
Duality Theorems
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Explores duality theorems in linear programming, emphasizing optimal solutions and degeneracies.
Forkas Lemma: Theorem of the Alternatives
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Explores the Forkas Lemma and the Theorem of the Alternatives in linear equation systems.
Separating Hyperplanes: Theory and Applications
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Explores separating hyperplanes for two sets X and Y, discussing conditions for separation and implications of convexity.
Game Theory: Minimax Theorem
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Explores zero-sum games and the minimax theorem in Game Theory, emphasizing optimal strategies.
Integer Programming Basics
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Introduces the basics of integer programming, including binary integer programs and constraint strategies.
Linear Programming Basics
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Explores linear programming basics, including basic solutions, feasible solutions, optimal solutions, and challenges in solving integer programming problems.
Integer Program Formulation
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Covers the process of formulating integer programs and improving solutions.
Branch and Bound: Heuristic Maximization
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Explains the Branch and Bound algorithm for heuristic maximization problems using LP relaxations and pruning techniques.
Introduction to Graph Theory
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Covers the basics of graph theory, including network flows, degrees of vertices, walks, and subgraphs.
Graph Theory and Network Flows
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Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.
Graph Theory Basics
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Introduces induced flows, basis matrices, and tree solutions in graph theory.
Simplex Algorithm: Basics
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Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.
Max Flow Problem
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Covers the max flow problem, weak duality, and the Ford-Fulkerson algorithm.
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