Laureate 2018 :

The 2018 laureate of the PGMO Thesis Award are:

 

 Nicolas BONIFAS

Geometric and Dual Approaches to Cumulative Scheduling

" The work of Nicolas Bonifas falls in the scope of constraint-based scheduling. In this framework, the most frequently encountered resource constraint is the cumulative, which enables the modeling of parallel processes. In his thesis, Nicolas studies the cumulative constraint with the help of tools rarely used in constraint programming (polyhedral analysis, linear programming duality, projective geometry duality) and propose two contributions for the domain. Cumulative strengthening is a means of generating tighter redundant cumulative constraints, analogous to the generation of cuts in integer linear programming. This is one of the first examples of a redundant global constraint. Energy Reasoning is an extremely powerful propagation for cumulative constraint, with hitherto a high complexity of O(n^3). Nicolas proposes an algorithm that computes this propagation with a O(n^2 log n) complexity, which is a significant improvement of this algorithm known for more than 25 years. Nicolas Bonifas, Geometric and Dual Approaches to Cumulative Scheduling . Université Paris-Saclay, 19/12/2017. http://www.theses.fr/2017SACLX119."

 

 Nicolas Flammarion

L'approximation stochastique et régression par moindres carrés avec applications en apprentissage automatique.

" Many problems in machine learning are naturally cast as the minimization of a smooth function defined on a Euclidean space. While small problems are efficiently solved by classical optimization algorithms, large-scale problems are typically solved with first-order techniques based on gradient descent. Nicolas Flammarion considers, in his thesis, the particular case of the quadratic loss. He addresses its minimization when gradients are only accessible through a stochastic oracle and proposes optimal algorithms in different cases. His work offers many perspectives of applications of the quadratic loss in machine learning. Clustering and estimation with shape constraints are the two first applications already considered. Nicolas Flammarion. Stochastic approximation and least-squares regression, with applications to machine learning. Paris Sciences et Lettres, 24/07/2017. http://www.theses.fr/2017PSLEE056. "

 

The jury for the 2018 edition was chaired by Mathilde Mougeot and composed of :

Members appointed by the PGMO Scientific Council :

Mathilde Mougeot, ENSIIE et Université Paris-Diderot, Présidente du Jury
Gabriel Peyré, ENS, CNRS
Nicolas Vieille, HEC

Members appointed by the ROADEF

Céline Gicquel, Université Paris-Sud
Sandra Ulrich Ngueveu, LAAS, CNRS
Michael Poss, Université de Montpellier

Members appointed by the group MODE of SMAI

Jean Baptiste Caillau, Université de la Côte d’Azur
Thierry Champion, Université de Toulon.
Aude Rondepierre, INSA de Toulouse

 

 

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Laureate 2017 :

The 2017 laureate of the PGMO Thesis Award are:

Vincent Cohen-Addad

From Practice to Theory : Approximation schemes for clustering and network design under the direction of Claire Mathieu

"This thesis contains pathbreaking and practically very important results concerning local search heuristics for clustering (k-means, k-median) and network design (traveling salesman, Steiner tree). It establishes some structural properties under which  these local search heuristics perform very well and even yield polynomial time approximation schemes for these problems."
 

Joon Kwon

Stratégies de descente miroir pour la minimisation du regret et l'approchabilité sous la direction de Rida Laraki & Sylvain Sorin

"The thesis begins with a remarkably clear presentation  of the basics of online linear optimization, regret minimization, mirror descent and approachability. The author develops the analysis of the classical problem of prediction with expert advice in which the outcome vector is assumed to be sparse, and design of optimal approachability strategies for the problem of prediction under partial monitoring. The author also shows how a continuous mirror descent motivates a large set of minimization algorithms in discrete time and the thesis ends with an elegant result bounding variations of convex functions."

The jury for the 2017 edition was chaired by Guillaume Carlier and composed of :

Members appointed by the ROADEF
Clarisse Dhaenens, CRIStAL, Université de Lille
Marcel Mongeau, ENAC
Sourour Elloumi, ENSTA

Members appointed by the group MODE of SMAI
Guillaume Carlier, CEREMADE (Président)
Anatoli Juditsky, Laboratoire Jean Kuntzmann, Grenoble
Jalal Fadili, ENSICAEN

Members appointed by the PGMO Scientific Council :
Luce Brotcorne, INRIA Lille
Julien Mairal, INRIA Grenoble
Jérôme Renault, Toulouse School of Economics

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Laureate 2016 :

The 2016 laureate of the PGMO Thesis Award are:

Pauline SARRABEZOLLES

Pauline Sarrabezolles obtained her PhD thesis in Applied Mathematics at Université Paris-Est and ENPC ParisTech under the supervision of Frédéric Meunier.
The title of the thesis is "colorful linear programming" and it stands at the intersection of discrete mathematics, combinatorics, optimization, graph theory and algorithmics.
Colorful linear programming is an extension of linear programming where the variables are assigned to different categories (colors) and their number in each category is bounded.
It has many applications in geometry and complex optimization problems.
She studied the complexity of some algorithms like a colorful version of the simplex algorithm and proved a combinatorial conjecture in connection with the colorful Carathéodory theorem.
The jury was impressed by the unique combination of various skills used by Pauline Sarrabezolles to solve these problems.

Bruno ZILIOTTO

Bruno Ziliotto obtained his PhD thesis in Applied Mathematics at Université de Toulouse under the supervision of Jérôme Renault. Its title is "Stratégies et paiements de long terme dans les jeux répétés à deux joueurs" and it is concerned with asymptotics of repeated zero-sum games, possibly with stochastic aspects. In particular it disproves a long-standing conjecture on the existence of a limit value and of a limit optimal strategy for the player. By the dynamic programming approach, this result has a link with the homogenization of stochastic Hamilton-Jacobi equations. More precisely, Bruno Ziliotto also found a striking counter-example of a non-convex Hamiltonian for which no stochastic homogenization occurs. The jury particularly appreciated the various and deep results obtained in different areas of mathematics and optimization.

The jury for the 2017 edition was chaired by Grégoire ALLAIRE and composed of :

Members appointed by the ROADEF
Eric Gourdin, Orange Labs
Christelle Jussien, Université d'Angers
Aziz Moukrim, UTC

Members appointed by the group MODE of SMAI
Francis Bach, INRIA Paris--Rocquencourt
Maitine Bergounioux, Université d'Orléans
Michel de Lara, ENPC

Members appointed by the PGMO Scientific Council :
Marianne Akian, INRIA Saclay--Ile-de-France
Grégoire Allaire, École polytechnique
Marie-Christine Costa, ENSTA

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Laureate 2015 :

Benjamin MARTIN

Benjamin Martin prepared his PhD thesis in Nantes in Computer Science after a Bachelor in Mathematics at the University of Nantes and a Master Degree in Computer Science at the University of Nantes too. The thesis directed and co-directed by Laurent Granvilliers, Alexandre Goldsztejn, Christophe Jermann is titled « Rigorous Algorithms for non-linear biobjective optimization ». The thesis deals with the interval based rigorous algorithm, i.e. with guaranteed results, to solve biobjective problems. The candidate proposes a certified continuation method that tracks locally a connected manifold of optimal solutions, which supplements other techniques from the literature. The proposed method  adapts finely to the shape of manifolds and deals with singularities resulting from inequality constraints in biobjective problems. Moreover, the candidate develops an interval Branch & Bound (B&B) algorithm that globally computes a verified enclosure of the optimal solutions. This method integrates constraint propagation techniques, noticeably exploiting bounds on the objectives, in order to enhance the solving process. The jury particularly appreciated the fact that the thesis presents both strong theoretical and applied results.

Samuel VAITER

Samuel Vaiter did is PhD thesis in Mathematics at the Univerity Paris Dauphine under the direction of Gabriel Peyré. He studied Computer Science and Mathematics at the ENS Lyon (Bachelor) and ENS Cachan (Master) respectively. The thesis is titled « Low Complexity Regularization of Inverse Problems ». This thesis is concerned with recovery guarantees and sensitivity analysis of variational regularization for noisy linear inverse problems. This is cast as a convex optimization problem by combining a data fidelity and a regularizing functional promoting solutions conforming to some notion of low complexity related to their non-smoothness points. This thesis makes a very nice contribution to the field of linear inverse problems, convex geometry and analysis. The candidate has provided a unified framework for analyzing the robustness (vis a vis noise) and sensitivity of solutions to the inverse problem. The results are sharp enough to recover some of the known results for special instances. At the same time, the framework is general enough to accommodate most regularizers used in practice. The jury particularly appreciated the fact that Samuel Vaiter was able to put under a single umbrella a series of techniques and results for treating a variety of problems.

The jury for the 2015 edition was chaired by Roberto Wolfler (Paris Nord LIPN) and composed of :

• Frédéric BONNANS, Jean-Baptiste HIRIART-URRUTY et Marc QUINCAMPOIX pour la SMAI 
• Luce BROTCORNE, Jean-Charles BILLAUT et Arnaud RENAUD pour la ROADEF
• Michel DE LARA, Laurent DUMAS et Roberto WOLFLER pour le PGMO

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Laureate 2014

Daniel HOEHENER

Daniel HOEHENER prepared his PhD thesis at Université Pierre et Marie Curie (Paris 6) on "Conditions d’optimalité pour des problèmes de contrôle optimal avec contraintes d’états'' (Optimality conditions for some optimal control problems with state constaints), supervised by Hélène Frankowska. His thesis includes several original results about second order conditions, proven under more general assumptions than the results already existing in literature, expressed in primal form, and involving both state and control constraints. The jury committee was impressed by the novelty of the results in a very technical domain, which also gave rise to excellent publications.

Miquel OLIU BARTON

Miquel OLIU BARTON also prepared his PhD thesis in Paris 6, under the supervision of Sylvain Sorin. The dissertation, on  "Jeux dynamiques à information incomplète en temps discret et continu" (Dynamical games with incomplete information in dicrete and continuous time) is mainly devoted to the long-time behavior in differential and repeated games and control problems, and to games with lack of information. The jury committee appreciated the impressive amount of works and the novelty of the results, and in particular the new proof that M. Oliu Barton gave of the existence of a limit for the value in discounted games, with new and self contained techniques.

The jury for the 2014 edition was chaired by Filippo SANTAMBROGIO and composed of :

• Jerome Bolte, Pierre Cardaliaguet  et Jean Bernard Lasserre pour la SMAI
• Pierre Lemaire, Frédéric Meunier et Safia Kedad-Sidhoum pour la ROADEF
• Alexandre d'ASPREMONT, Roberto WOLFER et Filippo SANTAMBROGIO pour le PGMO

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Laureate 2013

Olivier FERCOQ

Olivier Fercoq a fait sa thèse sous la direction de Stéphane Gaubert, Marianne Akian (X, CMAP +INRIA) et Mustapha Bouhtou (Orange Labs).
Le sujet était: "Optimisation de vecteurs propres de Perron et applications: du référencement de pages web à la chonothérapie."
Cette thèse constitue une "contribution majeure dans le domaine de l'optimisation de fonctions d'utilité sur l'ensemble des matrices positives" (selon l'un des rapporteurs). Elle présente à la fois un ensemble de résultats théoriques (propriétés, analyse de complexité,...) et des applications intéressantes.

Thibaut VIDAL

Thibaut Vidal a fait sa thèse sous la direction de Michel Gendreau, Theodor Crainic et Christian Prins, avec une co-tutelle entre les universités de Montréal et Troyes (CIRRELT+LOSI).
Le sujet était: "General solution approaches for multi-attribute vehicle routing and scheduling problems".
Cette thèse peut être vue comme "un recueil des résultats les plus intéressants et les plus marquants des dernières décades dans le domaine des recherche de solutions heuristiques pour les tournées de véhicules"(selon l'un des rapporteurs) et "un pas en avant dans la quête d'une approche générale de résolution d'une large palette de problèmes de tournées" (selon l'autre). Elle présente des résultats génériques concernant les méta-heuristiques pour les problèmes riches de tournées et pouvant être étendus à d'autres problèmes, une analyse complète de ces problèmes et s'appuie sur de lourdes réalisations informatiques.