Optimization Over Time-Varying Networks and Unbounded Information Delays

Solving optimization problems in multiagent systems involves information exchange between agents. The obtained solutions should be robust to delays and errors that arise from an unreliable wireless network, which typically connects the system. In today’s large-scale dynamic Internet of Things style scenarios, network topology changes evolves over time. this article, we present a simple distributed gradient-based framework associated algorithm. Convergence minimum given objective is shown under mild conditions on objective. A key feature our approach merely assume messages sent reach intended receiver, possibly delayed, with some positive probability. To best authors’ knowledge, ours first analysis such weak general conditions. We also discuss detail verifiability assumptions involved. This article makes technical contribution terms allowed class functions. Specifically, wherein function its sample-gradient locally Lipschitz continuous. theory developed herein supported by empirical results.