J
Jean-Christophe Pesquet
Researcher at Université Paris-Saclay
Publications - 387
Citations - 14714
Jean-Christophe Pesquet is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Convex optimization & Wavelet. The author has an hindex of 50, co-authored 364 publications receiving 13264 citations. Previous affiliations of Jean-Christophe Pesquet include University of Marne-la-Vallée & CentraleSupélec.
Papers
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Proceedings Article
Lifting schemes for joint coding of stereoscopic pairs of satellite images)
TL;DR: This work proposes a novel approach based on the concept of vector lifting scheme that does not generate one residual image but two compact multiresolution representations of the left and the right views, driven by the underlying disparity map.
Posted Content
A Proximal Approach for Sparse Multiclass SVM
TL;DR: In this article, a convex optimization approach for efficiently and exactly solving the multiclass SVM learning problem involving a sparse regularization and multiclass hinge loss formulated by Crammer and Singer is proposed.
Proceedings ArticleDOI
Random primal-dual proximal iterations for sparse multiclass SVM
TL;DR: This paper proposes two block-coordinate descent strategies for learning a sparse multiclass support vector machine by selecting a subset of features to be updated at each iteration, while the second one performs the selection among the training samples.
Journal ArticleDOI
Sparse signal reconstruction for nonlinear models via piecewise rational optimization
TL;DR: This work proposes a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate as a nonconvex minimization of the sum of a data fitting term and a penalization term, which relies on the so-called Lasserre relaxation of polynomial optimization.
Proceedings ArticleDOI
A block parallel majorize-minimize memory gradient algorithm
TL;DR: A Block Parallel Majorize-Minimize Memory Gradient (BP3MG) algorithm for solving large scale optimization problems, which combines a block coordinate strategy with an efficient parallel update to lead to significant computational time savings with respect to a sequential approach.