Publications associées à Adaptive Gradient Descent without Descent

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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

Adaptive Gradient Descent without Descent

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On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

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

Variational methods for finding periodic orbits in the incompressible Navier Stokes equations

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

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

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

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

A Newton Frank-Wolfe method for constrained self-concordant minimization

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

Computational Design of Weingarten Surfaces

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

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Computational Design of Weingarten Surfaces

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

Variational methods for finding periodic orbits in the incompressible Navier Stokes equations

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

A Newton Frank-Wolfe method for constrained self-concordant minimization

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