Lecture

Linear Programming Basics

Related lectures (32)

Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.

Covers optimization problems in path finding and portfolio allocation.

Covers the basics of linear programming, defining corners, extreme points, and feasible solutions within polyhedrons.

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

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

Covers classical optimization problems, brute force algorithms, and integer linear optimization.

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

Explores finding the initial Basic Feasible Solution (BFS) in a linear program.

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

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

Covers the basics of linear optimization, including equations, polyhedrons, feasible directions, and optimal solutions.

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

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

Explains linear constraints and the concept of a polyhedron in optimization problems.

Explores extreme points in linear programming and the role of constraints in finding optimal solutions.

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

Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.

Introduces the Simplex Method for optimal decision making in linear programming, covering basic and advanced concepts.

Covers the application of the two-phase Simplex algorithm to solve linear programming problems.

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