International audienceWe analyze alternating descent algorithms for minimizing the sum of a quadratic function and block separable non-smooth functions. In case the quadratic interactions between the blocks are pairwise, we show that the schemes can be accelerated, leading to improved convergence rates with respect to related accelerated parallel proximal descent. As an application we obtain very fast algorithms for computing the proximity operator of the 2D and 3D total variation
We consider the problem of finding the nearest point (by Euclidean distance) in a simplicial cone to...
In this paper we study decomposition methods based on separable approximations for mini-mizing the a...
We study and develop (stochastic) primal-dual block-coordinate descentmethods for convex problems ba...
International audienceWe analyze alternating descent algorithms for minimizing the sum of a quadrati...
We propose a new stochastic coordinate descent method for minimizing the sum of convex functions eac...
International audience<p>We propose a new randomized coordinate descent method for minimizing the s...
In this paper we prove a new complexity bound for a variant of Accelerated Coordinate Descent Method...
In this paper we prove a new complexity bound for a variant of the accelerated coordinate descent me...
This paper studies a flexible algorithm for minimizing a sum of component functions, each of which d...
International audienceA new result in convex analysis on the calculation of proximity operators in c...
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [11, 15]...
A new result in convex analysis on the calculation of proximity operators in certain scaled norms is...
International audienceOptimization methods play a central role in the solution of a wide array of pr...
We study the problem of minimizing the sum of a smooth convex function and a convex block-separable ...
We consider the problem of minimizing a smooth function over a feasible set defined as the Cartesian...
We consider the problem of finding the nearest point (by Euclidean distance) in a simplicial cone to...
In this paper we study decomposition methods based on separable approximations for mini-mizing the a...
We study and develop (stochastic) primal-dual block-coordinate descentmethods for convex problems ba...
International audienceWe analyze alternating descent algorithms for minimizing the sum of a quadrati...
We propose a new stochastic coordinate descent method for minimizing the sum of convex functions eac...
International audience<p>We propose a new randomized coordinate descent method for minimizing the s...
In this paper we prove a new complexity bound for a variant of Accelerated Coordinate Descent Method...
In this paper we prove a new complexity bound for a variant of the accelerated coordinate descent me...
This paper studies a flexible algorithm for minimizing a sum of component functions, each of which d...
International audienceA new result in convex analysis on the calculation of proximity operators in c...
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [11, 15]...
A new result in convex analysis on the calculation of proximity operators in certain scaled norms is...
International audienceOptimization methods play a central role in the solution of a wide array of pr...
We study the problem of minimizing the sum of a smooth convex function and a convex block-separable ...
We consider the problem of minimizing a smooth function over a feasible set defined as the Cartesian...
We consider the problem of finding the nearest point (by Euclidean distance) in a simplicial cone to...
In this paper we study decomposition methods based on separable approximations for mini-mizing the a...
We study and develop (stochastic) primal-dual block-coordinate descentmethods for convex problems ba...