Laurent DUMAS

Muhammad Zaid DAUHOO



Archives : course 2009 , 2010 , 2011

Course Objectives :

Many problems occuring in industry consist in minimizing (or maximizing) a certain cost function. This course is aimed to present various optimization methods in order to solve such problems.
After a general introduction on  numerical optimization, various optimization methods will be presented:  deterministic direct search (Nelder Mead, simplex), descent-type methods (gradient, newton, BFGS), stochastic methods (genetic algorithms, evolution strategies, PSO). A numerical implementation with Scilab of these differents methods will be done during the computer sessions.

Teachers : L . Dumas , T. Z. Boulmezaoud (Université de Versailles), M.Z. Dauhoo (University of Mauritius)

Course prerequisites :

No specific prerequisites are needed for this course.
It is accessible with basic tools in analysis (functions of n variables).

Syllabus (see timetable ): 

Mercredi 01 février 2012 (14-17h, TZB): introduction, convexity

Vendredi 03 février (8h-11h, LD): derivative free optimization (DFO) (slides, directFBG.sci, NelderMead.sci, MDS.sci)

Mercredi 15 février (14-17h, TZB): optimality conditions 1

Vendredi 17 février (8h-11h, MZD): descent algorithms 1

Mercredi 22 février (14-17h, TZB): optimality conditions 2

Vendredi 24 février (8h-11h, MZD): descent algorithms 2

Mercredi 29 février (14-17h, MZD): exercices + computer session : descent algorithms (TD, TP)

Vendredi 02 mars (8h-11h,LD): evolutionary algorithms (SA.sci,  ECP2012-GA.m, JJ.m)

Mercredi 07 mars (14-17h, TZB): linear programming

Vendredi 09 mars (8h-11h, LD): evolutionary algorithms (ECP2012-SR.sci)

Mercredi 14 mars (14-17h, MZD): exercises+omputer session : linear programming


Examen le Vendredi 30 mars 2012 (énoncé)



Optimisation continue : cours et exercices, J.F. Bonnans, Dunod, 2006.
Numercial optimization, theoretical and practical aspects
: JF Bonnans, JC Gilbert, C. Lemaréchal, C. Sagastizbal, Springer Verlag 2003.
Genetic Algorithms on search, optimization and machine learning : D. Goldberg, 1989

Articles (online):
An introdution to algorithms for non linear optimization : N. Gould, S. Leyffer

Training :

Examen 2011, rattrapage 2011

Examen 2010 , rattrapage 2010

 Examen 2009 , rattrapage 2009