Publications associées à Barrier Smoothing for Nonsmooth Convex Minimization

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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

Barrier Smoothing for Nonsmooth Convex Minimization

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Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

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

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Implications of ANEC for SCFTs in four dimensions

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

Stochastic Frank-Wolfe for Composite Convex Minimization

An adaptive primal-dual framework for nonsmooth convex minimization

Scalable Convex Optimization Methods for Semidefinite Programming

Universal and adaptive methods for robust stochastic optimization

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

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Scalable Convex Optimization Methods for Semidefinite Programming

Implications of ANEC for SCFTs in four dimensions

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

Stochastic Frank-Wolfe for Composite Convex Minimization

An adaptive primal-dual framework for nonsmooth convex minimization