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arXiv · 2109.03224

Accelerated Zeroth-order Algorithm for Stochastic Distributed Nonconvex Optimization

Shengjun Zhang·Colleen P. Bailey
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Abstract

This paper investigates how to accelerate the convergence of distributed optimization algorithms on nonconvex problems with zeroth-order information available only. We propose a zeroth-order (ZO) distributed primal-dual stochastic coordinates algorithm equipped with "powerball" method to accelerate. We prove that the proposed algorithm has a convergence rate of $\mathcal{O}(\sqrt{p}/\sqrt{nT})$ for general nonconvex cost functions. We consider solving the generation of adversarial examples from black-box DNNs problem to compare with the existing state-of-the-art centralized and distributed ZO algorithms. The numerical results demonstrate the faster convergence rate of the proposed algorithm and match the theoretical analysis.

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Shengjun Zhang, Colleen P. Bailey. 2021-09-07. Accelerated Zeroth-order Algorithm for Stochastic Distributed Nonconvex Optimization. https://arxiv.org/abs/2109.03224

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