Linear Programming: Optimization and Duality

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Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.

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

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

Covers optimization problems in path finding and portfolio allocation.

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

Covers linear programming duality and complementary slackness condition.

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

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

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

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.