Publications associées à The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

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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

The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

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Chattez avec Graph Search

Stable parameterization of continuous and piecewise-linear functions

Stochastic homogenization of degenerate integral functionals with linear growth

Localizing Unsynchronized Sensors With Unknown Sources

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Stochastic distributed learning with gradient quantization and double-variance reduction

Finite Littlestone Dimension Implies Finite Information Complexity

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

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

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

Stable parameterization of continuous and piecewise-linear functions

Stochastic homogenization of degenerate integral functionals with linear growth

Localizing Unsynchronized Sensors With Unknown Sources

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Stochastic distributed learning with gradient quantization and double-variance reduction

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Finite Littlestone Dimension Implies Finite Information Complexity

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

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