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Lecture
Linear Programming Basics
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Related lectures (32)
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Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Linear Programming: Weighted Bipartite Matching
Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.
Simplex Algorithm: Basics
Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.
Semi-Definite Programming
Covers semi-definite programming and optimization over positive semidefinite cones.
Approximation Algorithms
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Optimal Decision Making: The Simplex Method
Introduces the Simplex Method for optimal decision making in linear programming, covering basic and advanced concepts.
Cutset Formulation: MST Problem
Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.
Branch & Bound: Optimization
Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
Optimization Problems: Path Finding and Portfolio Allocation
Covers optimization problems in path finding and portfolio allocation.
Simplex Algorithm: Solution on a Vertex
Explores the simplex algorithm and how optimal solutions can be found on vertices of constraint polyhedra.
