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arXiv · 1904.04650

Gradient-Free Multi-Agent Nonconvex Nonsmooth Optimization

Davood Hajinezhad·Michael Zavlanos
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Abstract

In this paper, we consider the problem of minimizing the sum of nonconvex and possibly nonsmooth functions over a connected multi-agent network, where the agents have partial knowledge about the global cost function and can only access the zeroth-order information (i.e., the functional values) of their local cost functions. We propose and analyze a distributed primal-dual gradient-free algorithm for this challenging problem. We show that by appropriately choosing the parameters, the proposed algorithm converges to the set of first order stationary solutions with a provable global sublinear convergence rate. Numerical experiments demonstrate the effectiveness of our proposed method for optimizing nonconvex and nonsmooth problems over a network.

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Davood Hajinezhad, Michael Zavlanos. 2019-04-09. Gradient-Free Multi-Agent Nonconvex Nonsmooth Optimization. https://arxiv.org/abs/1904.04650

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