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Ph.D de

Ph.D
Group : Learning and Optimization

Uncertainties in Optimization

Starts on 01/09/2013
Advisor : TEYTAUD, Olivier

Funding :
Affiliation : Université Paris-Sud
Laboratory : LRI-TAO

Defended on 30/09/2016, committee :
Rapporteurs
-Thomas Jansen, Senior Lecturer, Aberystwyth University
-Dirk Arnold, Professeur, Dalhousie University

Examinateurs
-Sylvain Arlot, Professeur, Université Paris-Sud
-Emilie Kaufman, chargée de recherche, Université de Lille
-Vianney Perchet, Professeur, ENS Cachan
-Louis Wehenkel, Professeur, Université de Liège

Co-encadrant de thèse
Marc Schoenhauer, Directeur de recherche, Université Paris-Sud

Directeur de thèse
Olivier Teytaud, chargé de recherche, Université Paris-Sud

Research activities :

Abstract :
This research is motivated by the need to find out new methods to optimize a power system. In this field,
traditional management and investment methods are limited in front of highly stochastic problems which
occur when introducing renewable energies at a large scale. After introducing the various facets of power
system optimization, we discuss the continuous black-box noisy optimization problem and then some noisy
cases with extra features.

Regarding the contribution to continuous black-box noisy optimization, we are interested into finding lower
and upper bounds on the rate of convergence of various families of algorithms. We study the convergence of
comparison-based algorithms, including Evolution Strategies, in front of different strength of noise (small,
moderate and big). We also extend the convergence results in the case of value-based algorithms when dealing
with small noise. Last, we propose a selection tool to choose, between several noisy optimization algorithms,
the best one on a given problem.

For the contribution to noisy cases with additional constraints, the delicate cases, we introduce concepts from
reinforcement learning, decision theory and statistic fields. We aim to propose optimization methods closer
from the reality (in terms of modelling) and more robust. We also look for less conservative power system
reliability criteria.

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