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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

Espace localement convexe

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Stochastic distributed learning with gradient quantization and double-variance reduction

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Proximal Point Imitation Learning

Topology, Oxidation States, and Charge Transport in Ionic Conductors

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Accelerated SGD for Non-Strongly-Convex Least Squares

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

Invariant Integrals on Topological Groups and Applications

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

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Invariant Integrals on Topological Groups and Applications

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Stochastic distributed learning with gradient quantization and double-variance reduction

Proximal Point Imitation Learning

Topology, Oxidation States, and Charge Transport in Ionic Conductors

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

Accelerated SGD for Non-Strongly-Convex Least Squares

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

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods