Publications associées à Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

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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

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

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A ride time-oriented scheduling algorithm for dial-a-ride problems

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

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

Variance-Reduced Stochastic Learning Under Random Reshuffling

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Adaptive Gradient Descent without Descent

A Catalyst Framework for Minimax Optimization

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A ride time-oriented scheduling algorithm for dial-a-ride problems

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Variance-Reduced Stochastic Learning Under Random Reshuffling

A Catalyst Framework for Minimax Optimization

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

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

Adaptive Gradient Descent without Descent