Optimization and Analysis of Distributed Averaging With Memory

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Executive Summary

This paper analyzes the rate of convergence of a distributed averaging scheme making use of memory at each node. In conventional distributed averaging, each node computes an update based on its current state and the current states of their neighbours. Previous work observed the trajectories at each node converge smoothly and demonstrated via simulation that a predictive framework can lead to faster rates of convergence. This paper provides theoretical guarantees for a distributed averaging algorithm with memory.

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