Concept

Linear programming

Related lectures (32)

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

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

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

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

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

Covers optimization problems in path finding and portfolio allocation.

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

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

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

Explores convex polyhedra, linear programs, and their optimization importance.

Delves into duality in optimization, weak duality, cost certificates, and transforming non-linear programs into linear ones.

Explores mixed-integer linear programming, binary variables, 0-1 knapsack, assignment problems, and LP relaxation strength.

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

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

Explores integer programs, nonconvex optimization, constraints, and geometric aspects of linear programming for optimal solutions.

Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Explores optimal decision making through discrete optimization, emphasizing binary variables and practical applications.

Explores duality in linear programming, strong duality, complementary slackness, and the economic interpretation of dual variables as prices.

Explores dual translations in linear programming, emphasizing primal and dual formulations and the significance of invertible submatrices.