output
20132022
most citedFine-Grained Visual Classification of Aircraft

1.2k citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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7 papers · 1 filter

math.OC2022

On a fixed-point continuation method for a convex optimization problem

Jean-Baptiste Fest, Tommi Heikkilä, Ignace Loris +4

We consider a variation of the classical proximal-gradient algorithm for the iterative minimization of a cost function consisting of a sum of two terms, one smooth and the other pr…

math.OC2020★ 8 cited

Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization

Arthur Marmin, Marc Castella, Jean-Christophe Pesquet +1

We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate. Our method formulates the reconstruction problem as a…

math.OC2020★ 103 cited

Fixed Point Strategies in Data Science

Patrick L. Combettes, Jean-Christophe Pesquet

The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solv…

math.OC2020★ 28 cited

SPOQ -Over- Regularization for Sparse Signal Recovery applied to Mass Spectrometry

Afef Cherni, Emilie Chouzenoux, Laurent Duval +1

Underdetermined or ill-posed inverse problems require additional information for \ldd{d} sound solutions with tractable optimization algorithms. Sparsity yields consequent heuristi…

math.OC2018★ 2 cited

Nonlinear Acceleration of CNNs

Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont +1

The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration method capable of improving the rate of convergence of many optimization schemes such as gradient descend,…

math.OC2018★ 21 cited

A Proximal Approach for a Class of Matrix Optimization Problems

A. Benfenati, E. Chouzenoux, J. -C. Pesquet

In recent years, there has been a growing interest in mathematical models leading to the minimization, in a symmetric matrix space, of a Bregman divergence coupled with a regulariz…

math.OC2017★ 27 cited

A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression

Luis M. Briceno-Arias, Giovanni Chierchia, Emilie Chouzenoux +1

In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method. Our algorithm sweeps t…