Objectives of Programme's Learning Outcomes

• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Manage research, development and innovation.
• Understand unprecedented problems in computer science and its applications.
• Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Develop and integrate a high degree of autonomy.
• Aquire new knowledge independently.

Learning Outcomes of UE

Understand the fundamental notions and problems appearing in graph theory;study the corresponding algorithms;go deeply into algorithmic notions from the algorithm efficiency point of view;understand the fundamental problems and techniques of combinatorial optimization;illustrate some methods on some particular problems;show the utility of algorithms for solving practical problems in scheduling management, logistics,...

Content of UE

Basic notions of graph theory and data structure; study of classical graph theory problems : trees, shortest paths, connexity, flows;introduction to complexity theory : P and NP classes; study of classical combinatorial optimization problems : knapsack, set covering, travelling salesman; introduction to metaheuristics.

Prior Experience

Linear programming; duality; notion of algorithm.

Type of Assessment for UE in Q1

• Presentation and/or works
• Written examination

Q1 UE Assessment Comments

Reports of practical works : 20%.   Written examination covering both parts of the course: Graph theory  : (theory and exercises)  40% Combinatorial optimization : (theory and exercises)  40%

Type of Assessment for UE in Q3

• Presentation and/or works
• Written examination

Q3 UE Assessment Comments

Reports of practical works : 20%.   Written examination covering both parts of the course: Graph theory  : (theory and exercises)  40% Combinatorial optimization : (theory and exercises)  40%

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable