# Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization

## TL;DR

This paper introduces fast zeroth-order stochastic ADMM algorithms for nonconvex nonsmooth optimization, achieving optimal convergence rates and demonstrating effectiveness in complex machine learning tasks like black-box attacks.

## Contribution

It proposes novel zeroth-order stochastic ADMM methods for nonconvex problems with nonsmooth penalties, extending ADMM applicability to gradient-free scenarios.

## Key findings

- Achieve $O(1/T)$ convergence rate for nonconvex optimization.
- Effectively solve complex machine learning problems with multiple penalties.
- Validated through experiments on black-box classification and adversarial attacks.

## Abstract

Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning problems such as black-box attacks and bandit feedback, ADMM could fail because the explicit gradients of these problems are difficult or infeasible to obtain. Zeroth-order (gradient-free) methods can effectively solve these problems due to that the objective function values are only required in the optimization. Recently, though there exist a few zeroth-order ADMM methods, they build on the convexity of objective function. Clearly, these existing zeroth-order methods are limited in many applications. In the paper, thus, we propose a class of fast zeroth-order stochastic ADMM methods (i.e., ZO-SVRG-ADMM and ZO-SAGA-ADMM) for solving nonconvex problems with multiple nonsmooth penalties, based on the coordinate smoothing gradient estimator. Moreover, we prove that both the ZO-SVRG-ADMM and ZO-SAGA-ADMM have convergence rate of $O(1/T)$, where $T$ denotes the number of iterations. In particular, our methods not only reach the best convergence rate $O(1/T)$ for the nonconvex optimization, but also are able to effectively solve many complex machine learning problems with multiple regularized penalties and constraints. Finally, we conduct the experiments of black-box binary classification and structured adversarial attack on black-box deep neural network to validate the efficiency of our algorithms.