Add to ORKGNonconvex optimization is central in solving many machine learning problems, in which block-wise str...
The coordinate descent (CD) method is a classical optimization algorithm that has seen a revival of ...
In this work we show that randomized (block) coordinate descent methods can be accelerated by parall...
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [11, 15]...
The iteration complexity of the block-coordinate descent (BCD) type algorithm has been under extensi...
We study the problem of minimizing the sum of a smooth convex function and a convex block-separable ...
Abstract. In this paper we present a novel randomized block coordinate descent method for the minimi...
In this paper, we provide a unified iteration complexity analysis for a family of general block coor...
International audienceAs the number of samples and dimensionality of optimization problems related t...
Two types of low cost-per-iteration gradient descent methods have been extensively studied in par-al...
Block-coordinate descent algorithms and alternating minimization methods are fundamental optimizatio...
In this paper we propose a randomized block coordinate non-monotone gradient (RBCNMG) method for min...
We develop a novel randomised block coordinate primal-dual algorithm for a class of non-smooth ill-p...
In this paper we develop random block coordinate descent methods for minimizing large-scale linearl...
International audience<p>We propose a new randomized coordinate descent method for minimizing the s...
Nonconvex optimization is central in solving many machine learning problems, in which block-wise str...
The coordinate descent (CD) method is a classical optimization algorithm that has seen a revival of ...
In this work we show that randomized (block) coordinate descent methods can be accelerated by parall...
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [11, 15]...
The iteration complexity of the block-coordinate descent (BCD) type algorithm has been under extensi...
We study the problem of minimizing the sum of a smooth convex function and a convex block-separable ...
Abstract. In this paper we present a novel randomized block coordinate descent method for the minimi...
In this paper, we provide a unified iteration complexity analysis for a family of general block coor...
International audienceAs the number of samples and dimensionality of optimization problems related t...
Two types of low cost-per-iteration gradient descent methods have been extensively studied in par-al...
Block-coordinate descent algorithms and alternating minimization methods are fundamental optimizatio...
In this paper we propose a randomized block coordinate non-monotone gradient (RBCNMG) method for min...
We develop a novel randomised block coordinate primal-dual algorithm for a class of non-smooth ill-p...
In this paper we develop random block coordinate descent methods for minimizing large-scale linearl...
International audience<p>We propose a new randomized coordinate descent method for minimizing the s...
Nonconvex optimization is central in solving many machine learning problems, in which block-wise str...
The coordinate descent (CD) method is a classical optimization algorithm that has seen a revival of ...
In this work we show that randomized (block) coordinate descent methods can be accelerated by parall...