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

Finite Littlestone Dimension Implies Finite Information Complexity

Localizing Unsynchronized Sensors With Unknown Sources

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

Stochastic homogenization of degenerate integral functionals with linear growth

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

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

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