Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Bareilles, Gilles"'
Tame functions are a class of nonsmooth, nonconvex functions, which feature in a wide range of applications: functions encountered in the training of deep neural networks with all common activations, value functions of mixed-integer programs, or wave
Externí odkaz:
http://arxiv.org/abs/2311.13544
The Moment/Sum-of-squares hierarchy provides a way to compute the global minimizers of polynomial optimization problems (POP), at the cost of solving a sequence of increasingly large semidefinite programs (SDPs). We consider large-scale POPs, for whi
Externí odkaz:
http://arxiv.org/abs/2305.16122
We consider the problem of minimizing the composition of a nonsmooth function with a smooth mapping in the case where the proximity operator of the nonsmooth function can be explicitly computed. We first show that this proximity operator can provide
Externí odkaz:
http://arxiv.org/abs/2206.15053
Proximal methods are known to identify the underlying substructure of nonsmooth optimization problems. Even more, in many interesting situations, the output of a proximity operator comes with its structure at no additional cost, and convergence is im
Externí odkaz:
http://arxiv.org/abs/2012.12936
Publikováno v:
Annals of Operations Research, Springer Verlag, In press
Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario subproblems have to be solved at each iteration. In this paper, we in
Externí odkaz:
http://arxiv.org/abs/2009.12186
Autor:
Bareilles, Gilles, Iutzeler, Franck
In this paper, we study the interplay between acceleration and structure identification for the proximal gradient algorithm. We report and analyze several cases where this interplay has negative effects on the algorithm behavior (iterates oscillation
Externí odkaz:
http://arxiv.org/abs/1909.08944
Autor:
Bareilles, Gilles1 (AUTHOR), Iutzeler, Franck1 (AUTHOR) Franck.Iutzeler@univ-grenoble-alpes.fr, Malick, Jérôme1 (AUTHOR)
Publikováno v:
Mathematical Programming. Jun2023, Vol. 200 Issue 1, p37-70. 34p.
Akademický článek
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Autor:
Bareilles, Gilles
Publikováno v:
Statistics [math.ST]. Université Grenoble Alpes [2020-..], 2022. English. ⟨NNT : 2022GRALM037⟩
This thesis deals with the optimization of structured nonsmooth functions, which appear for example in machine learning and signal processing. In particular, we consider matrix functions which feature eigenvalue functions and the nuclear norm. Our ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::adb4ee525927deff2cf94ed7feb63b7d
https://theses.hal.science/tel-04061171/file/BAREILLES_2022_archivage.pdf
https://theses.hal.science/tel-04061171/file/BAREILLES_2022_archivage.pdf
Akademický článek
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