Quantitative Decision Making

Enseignants

Catanzaro Daniele; Porretta Luciano (supplée Catanzaro Daniele);

Langue
d'enseignement

Anglais

Contenu

This course, taught in english, is designed to develop both the ability to quantitatively analyze very large-scale practical problems in management science and to interpret and understand quantitative results in order to perform a more informed decision-making. Its aim is to introduce a broad range of optimization concepts and associated quantitative techniques with a view to helping the student appreciate the merits and limitations of these techniques as well as the data and technical requirements involved with their use.
The specific content of the course may change from year to year but often involves 

1. Introduction to Large Scale Optimization 
2. Projection, inverse projection, and their applications
3. Heuristics, Local Searches, Metaheuristics, and Matheuristics
4. Optimization methods for machine learning
5. Case studies

Méthodes d'enseignement

Slided & Blackboard lectures.

Modes d'évaluation
des acquis des étudiants

The examination method (e.g., project, written exam, or other forms) will be communicated by the lecturer during the first and *madatory* lecture of the course. 

Bibliographie

The lectures will be integrated with some capita selecta from the following references: (1) R. Kipp Martin. Large Scale Linear and Integer Optimization: A Unified Approach. Springer, 1999. (1) S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press 2004. (2) M. Conforti, G. Cornuejols, G. Zambelli. Integer Programming. Springer, 2014. (3) S. Heipcke. Applications of optimization with Xpress-MP. Dash Optimization, 2002.

Faculté ou entité
en charge

CLSM