Due to the COVID19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 15.0 h
Q1
This learning unit is not being organized during year 20202021.
Teacher(s)
Schaus Pierre;
Language
English
Main themes
 tree research exploration
 branch and bound
 relaxation (Lagrangian) and calculation of terminals
 local search
 mathematical programming
 constraint programming
 graph algorithms
 wide neighborhood research
 dynamic programming
 greedy algorithms and approximation algorithms
 multicriteria optimization
 optimization without derivative
 comparisons of algorithms
Aims
At the end of this learning unit, the student is able to :  
1 
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

Content
 dynamic programming
 branch and bound
 linear programming
 Lagrangian relaxation
 column generation
 local search
 constraint programming and sat
 graph algorithms: flows
 comparisons of optimization algorithms
Teaching methods
Due to the COVID19 crisis, the information in this section is particularly likely to change.
The presentation of the algorithms in the lecture will be accompanied by practical work (assignments / microprojects) requesting the implementation of an algorithm to solve a practical optimization problem. The evaluation work will be partially automated on the basis of the quality of the solutions found by the algorithms.
Evaluation methods
Due to the COVID19 crisis, the information in this section is particularly likely to change.
Much of the evaluation is associated to pratical work (30% of points across three assignments). The remaining 70% will be assessed in a conventional manner with a written or oral examination. Projects can not be redone in the second session.
Other information
Background:
 LSINF1121
Online resources
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Master [120] in Data Science Engineering
Master [120] in Data Science: Information Technology