Abstract. The notion of soft thresholding plays a central role in problems from various areas of applied mathematics, in which the ideal solution is known to possess a sparse decomposition in some orthonormal basis. Using convex-analytical tools, we extend this notion to that of proximal thresholding and investigate its properties, providing in particular several characterizations of such thresholders. We then propose a versatile convex variational formulation for optimization over orthonormal bases that covers a wide range of problems, and establish the strong convergence of a proximal thresholding algorithm to solve it. Numerical applications to signal recovery are demonstrated
International audience—We develop a projected Nesterov's proximal-gradient (PNPG) approach for spars...
Abstract. An extension ofthe proximal minimization algorithm is considered where only some of the mi...
This thesis is concerned with a class of methods known collectively as iterative thresholding algori...
Optimization in image processing Ideal image x described by pieces of informations (or just beliefs....
International audienceThe proximity operator of a convex function is a natural extension of the noti...
Abstract The proximal gradient algorithm is an appealing approach in finding solutions of non-smooth...
International audience—This paper is concerned with designing efficient algorithms for recovering sp...
Abstract. Proximal methods have recently been shown to provide ef-fective optimization procedures to...
We discuss a number of novel steplength selection schemes for proximal-based convex optimization alg...
We study the ℓ1 regularized least squares optimization problem in a separable Hilbert space. We show...
AbstractThis article provides a variational formulation for hard and firm thresholding. A related fu...
International audienceIn this note, we consider a special instance of the scaled, inexact and adapti...
In the present paper, we investigate a linearized p roximal algorithm (LPA) for solving a convex com...
The proximal point algorithm has known these last years many developments connected with the expansi...
Following the works of R.T. Rockafellar, to search for a zero of a maximal monotone operator, and of...
International audience—We develop a projected Nesterov's proximal-gradient (PNPG) approach for spars...
Abstract. An extension ofthe proximal minimization algorithm is considered where only some of the mi...
This thesis is concerned with a class of methods known collectively as iterative thresholding algori...
Optimization in image processing Ideal image x described by pieces of informations (or just beliefs....
International audienceThe proximity operator of a convex function is a natural extension of the noti...
Abstract The proximal gradient algorithm is an appealing approach in finding solutions of non-smooth...
International audience—This paper is concerned with designing efficient algorithms for recovering sp...
Abstract. Proximal methods have recently been shown to provide ef-fective optimization procedures to...
We discuss a number of novel steplength selection schemes for proximal-based convex optimization alg...
We study the ℓ1 regularized least squares optimization problem in a separable Hilbert space. We show...
AbstractThis article provides a variational formulation for hard and firm thresholding. A related fu...
International audienceIn this note, we consider a special instance of the scaled, inexact and adapti...
In the present paper, we investigate a linearized p roximal algorithm (LPA) for solving a convex com...
The proximal point algorithm has known these last years many developments connected with the expansi...
Following the works of R.T. Rockafellar, to search for a zero of a maximal monotone operator, and of...
International audience—We develop a projected Nesterov's proximal-gradient (PNPG) approach for spars...
Abstract. An extension ofthe proximal minimization algorithm is considered where only some of the mi...
This thesis is concerned with a class of methods known collectively as iterative thresholding algori...