Publications associées à Non-convex, non-local functionals converging to the total variation

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A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Non-convex, non-local functionals converging to the total variation

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Stochastic homogenization of degenerate integral functionals with linear growth

Localizing Unsynchronized Sensors With Unknown Sources

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Stochastic distributed learning with gradient quantization and double-variance reduction

A first-order primal-dual method with adaptivity to local smoothness

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Adaptive Gradient Descent without Descent

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Convex Combination of Diffusion Strategies Over Networks

On the convergence of stochastic primal-dual hybrid gradient

Localizing Unsynchronized Sensors With Unknown Sources

A first-order primal-dual method with adaptivity to local smoothness

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Adaptive Gradient Descent without Descent

Convex Combination of Diffusion Strategies Over Networks

Stochastic distributed learning with gradient quantization and double-variance reduction

Stochastic homogenization of degenerate integral functionals with linear growth

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

On the convergence of stochastic primal-dual hybrid gradient