Publications
5 selected recent publications
(one/year over the last 5 years)

Harnessing structure in composite nonsmooth minimization
Gilles Bareilles, Franck Iutzeler, Jerome Malick
To appear in
SIAM Journal on Optimization (2023) [
preprint]

Multiagent online optimization with delays: Asynchronicity, adaptivity, and optimism
YG. Hsieh, F. Iutzeler, J. Malick, P. Mertikopoulos

The lastiterate convergence rate of optimistic mirror descent in stochastic variational inequalities
Waiss Azizian, Franck Iutzeler, Jerome Malick, Panayotis Mertikopoulos

Proximal gradient methods with adaptive subspace sampling
Dmitry Grishchenko, Franck Iutzeler, Jerome Malick

Eventual convexity of chance constraints with elliptical distributions
Wim van Ackooij, Jerome Malick
My personal top5

Local linear convergence for alternating nonconvex projections
Adrian Lewis, Russel Luke, Jerome Malick
Foundations of Computational Mathematics (2009) [
paper][
preprint]
The first study in the nonconvex setting of the simple and fundamental algorithm of alternating projections.
This gives nice illustrations on how geometry controls convergence:
see e.g. the
idea of the proof of Theorem 5.2 (page 14 in the
preprint).

Cutgenerating functions and Sfree sets
M. Conforti, G. Cornuejols, A. Daniilidis, C. Lemarechal, J. Malick
This paper establishes the basis of a theory of cutgenerating functions, a fundamental tool in combinatorial optimization. Nice fact: this theory is grounded on convex geometry  and even subtle new results
(we needed the smallest representation of prepolars of convex sets; see Theorem 3.8). The proof of Theorem 5.1 required creativity and abnegation;
we got both from
geometrical constructions.

UNewton methods for nonsmooth convex minimization
Scott Miller, Jerome Malick
Newton is everywhere ! This paper reveals the connection between (i) UNewton methods from nonsmooth optimization, (ii) Riemannian Newton methods, and (iii) standard Newton methods (SQP) of nonlinear optimization
The paper is not easy to read; you may prefer this
one, which is a followup treating the nonconvex case.

Decomposition algorithm for largescale twostage unitcommitment
Wim van Ackooij, Jerome Malick
How to save carbon emission (and money)? Include stochasticity in the electricity park management model. We propose an effective algorithm to solve resulting largescale optimization problem.

Harnessing structure in composite nonsmooth minimization
Gilles Bareilles, Franck Iutzeler, Jerome Malick
To appear in
SIAM Journal on Optimization [
preprint]
My lastest paper already in my top five ? I might have given too much heart in this one... Anyway, if you think you know all about the proximal mapping, check out Theorem 3.2 (page 9) and you'll be surprised! The prox implicity identifies relevant submanifolds, for a specific range of proxparameters, as illustrated on this
picture for maxfunctions. Property both surprising and useful to design superlinear algorithms minimizing nonsmooth functions, as the maxeigenvalue of a parametrized matrice.
Last highlight: "apply mechanics to maths" as JJ Moreau said... or viceversa ?