  Distributed multi-agent optimization finds many applications in distributed
learning, control, estimation, etc. Most existing algorithms assume knowledge
of first-order information of the objective and have been analyzed for convex
problems. However, there are situations where the objective is nonconvex, and
one can only evaluate the function values at finitely many points. In this
paper we consider derivative-free distributed algorithms for nonconvex
multi-agent optimization, based on recent progress in zero-order optimization.
We develop two algorithms for different settings, provide detailed analysis of
their convergence behavior, and compare them with existing centralized
zero-order algorithms and gradient-based distributed algorithms.
