On the Performance of Exact Diffusion over Adaptive Networks

Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under proper conditions. However, their performance under stochastic and adaptive settings remains unclear. It is still unknown whether bias-correction is beneficial in stochastic settings. By studying exact diffusion and examining its steady-state performance under stochastic scenarios, this paper provides affirmative results. It is shown that the correction step in exact diffusion can lead to better steady-state performance than traditional methods.


Published in:
2019 Ieee 58Th Conference On Decision And Control (Cdc), 4898-4903
Presented at:
58th IEEE Conference on Decision and Control (CDC), Nice, FRANCE, Dec 11-13, 2019
Year:
Jan 01 2019
Publisher:
New York, IEEE
ISSN:
0743-1546
ISBN:
978-1-7281-1398-2
Keywords:
Laboratories:




 Record created 2020-09-05, last modified 2020-10-25


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