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)
Catanzaro Daniele;
Language
English
Prerequisites
- MQANT1110 - Mathématiques de gestion 1
- MQANT1227 - Mathématiques de gestion 2
- MQANT1329 - Optimisation
- MQANT1223 - Informatique et algorithmique
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
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 : | |
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This course contributes to develop the following competencies.
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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
This course provides an introduction to algorithmic problem solving. Its main goal is to learn how to implement solution approaches for different type of problems involving search and optimization features. It covers the introduction to graph theory, classical algorithms on graphs, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming. It pays attention on the practical importance of specific classes of optimization problems in management science and motivate the students to develop algorithms to solve them.
The course includes in particular the following topics:
The course includes in particular the following topics:
- Recursion
- Fundation of data structures: Graphes
- Basic algorithms on graphs
- Well Solved Optimization Problems in Management Science - Part I: Spanning Trees
- Well Solved Optimization Problems in Management Science - Part II: Shortest Paths
- Hard Optimization Problems in Management Science - Part I - Spanning Trees with constraints
- Hard Optimization Problems in Management Science - Part I - Shortest Paths with constraints
- Finding the optimum via Branch-&-Bound
- Introduction to Heuristics, Local Searches and Metaheuristics
Teaching methods
Blackboard lectures.
Evaluation methods
Continuous evaluation, including an individual project with final exam. Due to the particular nature of this course, the evaluation will consists of only one exam per year. Depending upon the academic calendar, the scheudling of such exam may vary from year to year and will be communicated by the lecturer in charge during the first (and mandatory) lecture of the course.
Online resources
Bibliography
The lectures will be integrated with some capita selecta from the following references: (1) Cormen, Thomas, Charles Leiserson, Ronald Rivest, and Clifford Stein. Introduction to Algorithms. 3rd ed. MIT Press, 2009; (2) B, Eckel. Thinking in Java, 4th Edition. Prentice Hall, 2006. (3) L. Wolsey. Integer Programming, Wiley, 1998. (4) M. Gendreau and J. Y. Potvin. Handbook of Metaheuristics. Springer, 2010.
Faculty or entity
CLSM
