Teacher(s)

Catanzaro Daniele;

Language

English

Prerequisites

• MQANT1110 - Mathématiques de gestion 1
• MQANT1227 - Mathématiques de gestion 2
• MQANT1329 - Optimisation
• MQANT1223 - Informatique et algorithmique 

Main themes

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming. It pays particular attention on the practical importance of specific classes of optimization problems in management science and motivate the students to develop algorithms to solve them.   

Aims

At the end of this learning unit, the student is able to :
1	This course contributes to develop the following competencies. Knowledge Scientific reasoning and systematic approach Project management Leadership At the end of this course, students will: Improve their strategical thinking skills Acquire fundamental knowledge on the modeling and the resolution of practical problems Apply the appropriate techniques to propose a useful solution.

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

1. Recall of fundation of data structures
2. Iterations vs Recursion
3. Dynamic Programming Part I - Well Solved Optimization Problems in Management Science: Spanning Trees
4. Dynamic Programming Part II - Well Solved Optimization Problems in Management Science: Shortest Paths
5. Dynamic Programming Part III - Well Solved Optimization Problems in Management Science: Network Flows
6. Dynamic Programming Part VI - Well Solved Optimization Problems in Management Science: Matching
7. Hard Optimization Problems in Management Science - Finding the optimum via Branch-&-Bound
8. Hard Optimization Problems in Management Science - Heuristics
9. Hard Optimization Problems in Management Science - Local Searches
10. Hard Optimization Problems in Management Science - Metaheuristics

Teaching methods

Blackboard lectures.

Evaluation methods

Individual project with final report and oral exam.

Faculty or entity

CLSM