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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

Fonction convexe

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

Proximal Point Imitation Learning

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

A convex set of robust D—stabilizing controllers using Cauchy's argument principle

Accelerated SGD for Non-Strongly-Convex Least Squares

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

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

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

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

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

Proximal Point Imitation Learning

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

A convex set of robust D—stabilizing controllers using Cauchy's argument principle

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Accelerated SGD for Non-Strongly-Convex Least Squares

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

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation