Linear Programming Basics

Related lectures (30)

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

Covers optimization problems in path finding and portfolio allocation.

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.

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

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

Covers exercises on the Simplex Algorithm, optimizing solutions subject to linear constraints.

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.

Covers the modelling and optimization of energy systems, focusing on solving optimization problems with constraints and variables.