To provide insight in theory and development of practical methods for basic and advanced linear programming.

Part 1:
Basic theory and algorithms of linear optimization:
- Linear optimization
- polyhedra and polytopes
- the simplex algorithm
- duality
- linear inequalities and Farkas' lemma
- sensitivity analysis

Part 2:
Advanced linear optimization methods
- the revised simplex method and column generation
- Dantzig-Wolfe and Benders' decomposition
- network flow problems
- the ellipsoid method
- an interior point method
- integer programming formulations and solution methods

Prerequisites

Basic knowledge (bachelor level) of linear algebra and graph theory.