Publications associées à Composite convex minimization involving self-concordant-like cost functions

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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

Composite convex minimization involving self-concordant-like cost functions

Graph Chatbot

Chattez avec Graph Search

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

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

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Mathematical Foundations of Robust and Distributionally Robust Optimization

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

Adaptive Gradient Descent without Descent

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Stochastic Frank-Wolfe for Composite Convex Minimization

Universal and adaptive methods for robust stochastic optimization

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

Adaptive Gradient Descent without Descent

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Stochastic Frank-Wolfe for Composite Convex Minimization

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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

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

Mathematical Foundations of Robust and Distributionally Robust Optimization