Linear Programming: Two-phase Simplex Algorithm

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Covers sensitivity analysis in linear programming, focusing on optimal solutions and their sensitivities to changes.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Covers semi-definite programming and optimization over positive semidefinite cones.

Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Explores the formulation of the auxiliary problem in linear optimization and its role in optimal decision-making.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Explores the concept of duality in linear programming, discussing the relationship between primal and dual problems.

Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.

Introduces the two-phase simplex algorithm and explores duality in linear programming.