International audienceWe revisit the proofs of convergence for a first order primal-dual algorithm for convex optimization which we have studied a few years ago. In particular, we prove rates of convergence for a more general version, with simpler proofs and more complete results. The new results can deal with explicit terms and nonlinear proximity operators in spaces with quite general norms
This note discusses proofs for convergence of first-order methods based on simple potential-function...
This thesis focuses on three themes related to the mathematical theory of first-order methods for co...
We study the extension of the Chambolle--Pock primal-dual algorithm to nonsmooth optimization proble...
International audienceWe revisit the proofs of convergence for a first order primal-dual algorithm f...
We study a first-order primal-dual algorithm for convex optimization problems with known saddle-poin...
Consider the utilization of a Lagrangian dual method which is convergent for consistent convex optim...
This thesis focuses on two topics in the field of convex optimization: preprocessing algorithms for ...
Abstract. Primal-dual splitting schemes are a class of powerful algorithms that solve compli-cated m...
When solving a convex optimization problem through a Lagrangian dual reformulation subgradient optim...
First-order methods for solving convex optimization problems have been at the forefront of mathemati...
We present a numerical iterative optimization algorithm for the minimization of a cost function cons...
We present a primal-dual algorithmic framework to obtain approximate solutions to a prototypical con...
We study and develop (stochastic) primal--dual block-coordinate descent methods for convex problems ...
In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algor...
HAL v1 = arXiv:1803.10576v3 (see also v2)International audienceWe investigate the convergence of a r...
This note discusses proofs for convergence of first-order methods based on simple potential-function...
This thesis focuses on three themes related to the mathematical theory of first-order methods for co...
We study the extension of the Chambolle--Pock primal-dual algorithm to nonsmooth optimization proble...
International audienceWe revisit the proofs of convergence for a first order primal-dual algorithm f...
We study a first-order primal-dual algorithm for convex optimization problems with known saddle-poin...
Consider the utilization of a Lagrangian dual method which is convergent for consistent convex optim...
This thesis focuses on two topics in the field of convex optimization: preprocessing algorithms for ...
Abstract. Primal-dual splitting schemes are a class of powerful algorithms that solve compli-cated m...
When solving a convex optimization problem through a Lagrangian dual reformulation subgradient optim...
First-order methods for solving convex optimization problems have been at the forefront of mathemati...
We present a numerical iterative optimization algorithm for the minimization of a cost function cons...
We present a primal-dual algorithmic framework to obtain approximate solutions to a prototypical con...
We study and develop (stochastic) primal--dual block-coordinate descent methods for convex problems ...
In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algor...
HAL v1 = arXiv:1803.10576v3 (see also v2)International audienceWe investigate the convergence of a r...
This note discusses proofs for convergence of first-order methods based on simple potential-function...
This thesis focuses on three themes related to the mathematical theory of first-order methods for co...
We study the extension of the Chambolle--Pock primal-dual algorithm to nonsmooth optimization proble...