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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

Localizing Unsynchronized Sensors With Unknown Sources

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

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

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

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

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Mathematical Foundations of Robust and Distributionally Robust Optimization

Proximity Operators of Discrete Information Divergences

On the convergence of stochastic primal-dual hybrid gradient