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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

Finding minimum area simple pentagons

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

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

Mathematical Foundations of Robust and Distributionally Robust Optimization

Stochastic distributed learning with gradient quantization and double-variance reduction

On the convergence of stochastic primal-dual hybrid gradient

Stochastic Three-Composite Convex Minimization with a Linear Operator

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

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

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