Combinatorial optimization

Related lectures (32)

Page 3 of 4

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

Explores optimization with constraints using KKT conditions and interior point algorithm on two examples of quadratic programming.

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

Explores optimization problems in standard form, convex optimization, and optimality criteria.

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

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

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

Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.

Explains the Branch and Bound algorithm for heuristic maximization problems using LP relaxations and pruning techniques.

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