Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 15.0 h
Q1
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
- multi-criteria 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:
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The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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
The presentation of the algorithms in the lecture will be accompanied by practical work (assignments / micro-projects) 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
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 Engineering
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Master [120] in Data Science : Statistic
Master [120] in Data Science: Information Technology