Distributed Constrained Online Nonconvex Optimization with Compressed Communication

Abstract

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal-dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an $\mathcal{O}( {{T^{\max \{ {1 - {θ_1},{θ_1}} \}}}} )$ network regret bound and an $\mathcal{O}( {T^{1 - {θ_1}/2}} )$ network cumulative constraint violation bound, where $T$ is the number of iterations and ${θ_1} \in ( {0,1} )$ is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to $\mathcal{O}( {T^{1 - {θ_1}}} )$. These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results.