Combinatorial optimization

linma2450  2022-2023  Louvain-la-Neuve

Combinatorial optimization
5.00 credits
30.0 h + 22.5 h
Basic knowledge of linear programming and the simplex algorithm
Main themes
The course is about different ways to solve optimization problems with discrete or integer variables, which are used to handle indivisibilities, or on/off decisions, such as choosing an edge in a graph, buying a machine, using a warehouse, etc. Such problems arise in scheduling trains or aircraft, constructing a tour in a graph, drawing up a production plan for electricity generation, etc. The theory involves the study of polyhedra, matrices, graphs and aspects of complexity and the development of tight formulations. The algorithmic approaches covered include implicit enumeration and cutting planes (branch-and-cut), Lagrangian relaxation, dynamic programming and approximation algorithms.
Learning outcomes

At the end of this learning unit, the student is able to :

1 Learning outcomes:
  • AA1: 1,2
More specifically, at the end of the course, the student should be able to :
  • formulate different combinatorial problems as integer programmes
  • explore different formulations for a same problem
  • find lower and upper bounds to the solution of an integer programme
  • recognize and solve some integer programmes that are solvable in polynomial time
  • recognize some integer programmes that are hard to solve (NP-hard)
  • apply various techniques (branch-and-bound, Lagrangian relaxation, heuristics) to solve hard problems approximately
Tranversal learning outcomes:
  • Use of Matlab or other softwares to solve medium-size problems
  1. Formulation of combinatorial optimization and integer programming problems.
  2. Finding bounds on the optimal value and using them to prove optimality
  3. Recognizing and solving certain easy problems - network flows, trees, matching and assignment problems  
  4. Introduction to the distinction between easy and hard problems: NP-hardness
  5. Intelligent enumeration - the branch-and-bound algorithm
  6. Lagrangian relaxation
  7. Introduction to cutting plane algorithms
  8. Heuristic methods to find good solutions quickly
Teaching methods
Lectures, possibly complemented by individual discovery of certain topics, and supervised exercises sessions. These activities take place in the classroom or in “co-modal” form depending on practical constraints and on the number of students present.
Students also complete one or several more advanced homework, using an optimization software.
Evaluation methods
A written exam will count for 85% of the grade. The remaining 15% are obtained through homework (between 1 and 3 problem sets to be solved during the semester).
Online resources
Integer Programming, L.A. Wolsey, Wiley, New York 1998.
Teaching materials
  • Documents sur la page Moodle / Documents on the Moodle page
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Mathematics

Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology