2012 - IEEE Fellow For contributions to statistical methods for signal recovery
Jean-Christophe Pesquet spends much of his time researching Mathematical optimization, Convex optimization, Algorithm, Wavelet and Optimization problem. His research in Mathematical optimization intersects with topics in Deconvolution, Convergence, Hilbert space, Convex analysis and Image restoration. His Convex optimization study combines topics from a wide range of disciplines, such as Iterative method, Convex function and Monotone polygon.
His work deals with themes such as Orthonormal basis and Multiresolution analysis, which intersect with Algorithm. Within one scientific family, Jean-Christophe Pesquet focuses on topics pertaining to Differentiable function under Optimization problem, and may sometimes address concerns connected to Inverse problem, Critical point, Subspace topology and Sparse image. His research integrates issues of Conic optimization, Linear subspace and Proper convex function in his study of Proximal Gradient Methods.
Jean-Christophe Pesquet spends much of his time researching Algorithm, Mathematical optimization, Artificial intelligence, Convex optimization and Wavelet. He has included themes like Image restoration and Iterative reconstruction in his Algorithm study. His Mathematical optimization research is multidisciplinary, incorporating elements of Image processing, Convergence, Inverse problem and Convex analysis.
He interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence. His Convex optimization research is multidisciplinary, incorporating perspectives in Monotone polygon, Minification, Iterative method, Convex function and Parallel algorithm. His research investigates the connection with Monotone polygon and areas like Hilbert space which intersect with concerns in Applied mathematics.
Jean-Christophe Pesquet focuses on Artificial neural network, Artificial intelligence, Algorithm, Applied mathematics and Convex optimization. His Artificial intelligence course of study focuses on Computer vision and Imaging phantom. His Interior point method study in the realm of Algorithm connects with subjects such as Operator.
His Applied mathematics study also includes fields such as
Jean-Christophe Pesquet mainly focuses on Artificial neural network, Applied mathematics, Algorithm, Artificial intelligence and Lipschitz continuity. In general Artificial neural network, his work in Recurrent neural network is often linked to Layer linking many areas of study. His studies in Applied mathematics integrate themes in fields like Optimization problem, Rational function and Nonlinear system.
His Algorithm research includes elements of Optimization algorithm, Lambert W function and Training set. He combines subjects such as Image processing and Robustness with his study of Lipschitz continuity. His Iterated function study deals with Fixed point intersecting with Convex optimization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Proximal Splitting Methods in Signal Processing
Patrick Louis Combettes;Jean-Christophe Pesquet.
Fixed-point algorithms for inverse problems in science and engineering, 2011, ISBN 978-1-4419-9568-1, págs. 185-212 (2011)
Time-invariant orthonormal wavelet representations
J.-C. Pesquet;H. Krim;H. Carfantan.
IEEE Transactions on Signal Processing (1996)
A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery
P.L. Combettes;J.-C. Pesquet.
IEEE Journal of Selected Topics in Signal Processing (2007)
Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators
Patrick Louis Combettes;Jean-Christophe Pesquet.
Set-valued and Variational Analysis (2012)
A proximal decomposition method for solving convex variational inverse problems
Patrick Louis Combettes;Jean-Christophe Pesquet.
Inverse Problems (2008)
A variational formulation for frame-based inverse problems
Caroline Chaux;Patrick Louis Combettes;Jean-Christophe Pesquet;Valérie R. Wajs.
Inverse Problems (2007)
Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems
Nikos Komodakis;Jean-Christophe Pesquet.
IEEE Signal Processing Magazine (2015)
Image restoration subject to a total variation constraint
P.L. Combettes;J.-C. Pesquet.
IEEE Transactions on Image Processing (2004)
Proximal Thresholding Algorithm for Minimization over Orthonormal Bases
Patrick L. Combettes;Jean-Christophe Pesquet.
Siam Journal on Optimization (2007)
Long-range dependence and heavy-tail modeling for teletraffic data
O. Cappe;E. Moulines;J.-C. Pesquet;A.P. Petropulu.
IEEE Signal Processing Magazine (2002)
Profile was last updated on December 6th, 2021.
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