Publications associées à A Catalyst Framework for Minimax Optimization

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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

A Catalyst Framework for Minimax Optimization

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

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

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

Stochastic distributed learning with gradient quantization and double-variance reduction

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

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

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

Convex optimization in sums of Banach spaces

A Catalyst Framework for Minimax Optimization

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

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

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Stochastic distributed learning with gradient quantization and double-variance reduction

A Catalyst Framework for Minimax Optimization

Convex optimization in sums of Banach spaces

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

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

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