Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation
Abstract
We study distributed stochastic nonconvex optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of the expected value of a smooth (possibly nonconvex) function (the agents' sum-utility) plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques, leveraging dynamic consensus as a mechanism to track the average gradient among the agents, and recursive averaging to recover the expected gradient of the sum-utility function. Almost sure convergence to (stationary) solutions of the nonconvex problem is established. Finally, the method is applied to distributed stochastic training of neural networks. Numerical results confirm the theoretical claims, and illustrate the advantages of the proposed method with respect to other methods available in the literature.
Keywords
Cite
@article{arxiv.2004.14882,
title = {Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation},
author = {Paolo Di Lorenzo and Simone Scardapane},
journal= {arXiv preprint arXiv:2004.14882},
year = {2020}
}
Comments
Proceedings of 2019 Asilomar Conference on Signals, Systems, and Computers