Download our brochure

Master in Information and Computer Sciences

Optimisation for Computer Science
Module:Module 2.11, Semester 2
Objective: Different problems have different nature. In terms of complexity some problems are called intractable and can not be solved by classical computers. But there are also many other aspects of the nature of optimisation problems such as linearity, convexity, continuity, dynamicity, randomness that may lead the choice of different optimisation techniques
Course learning outcomes: * Characterize problems
* Identify the key concepts related to optimisation techniques
* Use optimization frameworks
* Implement optimization algorithms
* Validate optimization algorithms and results
* Validate approaches for solving optimization problems

Description: This lecture confront the students to real instances of such problems. They are first asked to model the problem and next proposed solutions include exact methods, relaxations, approximations, heuristics and meta-heuristics. And these practical study cases are supported by the theoretical lectures on Problem Solving (1st semester)
Organization:The students are directly involved into research teams helping them to solve real problems illustrating the various approaches.
A mid term review is organized during which students present the problem model and at the end of the course, students will present the final results
Language: English
Lecturer: BOUVRY Pascal
Rating: Project: 100%