Decentralized Optimization Over Tree Graphs

Yuning Jiang (), Dimitris Kouzoupis (), Haoyu Yin (), Moritz Diehl () and Boris Houska ()
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Yuning Jiang: Shanghai Tech University
Dimitris Kouzoupis: University of Freiburg
Haoyu Yin: Shanghai Tech University
Moritz Diehl: University of Freiburg
Boris Houska: Shanghai Tech University

Journal of Optimization Theory and Applications, 2021, vol. 189, issue 2, No 3, 384-407

Abstract: Abstract This paper presents a decentralized algorithm for non-convex optimization over tree-structured networks. We assume that each node of this network can solve small-scale optimization problems and communicate approximate value functions with its neighbors based on a novel multi-sweep communication protocol. In contrast to existing parallelizable optimization algorithms for non-convex optimization, the nodes of the network are neither synchronized nor assign any central entity. None of the nodes needs to know the whole topology of the network, but all nodes know that the network is tree-structured. We discuss conditions under which locally quadratic convergence rates can be achieved. The method is illustrated by running the decentralized asynchronous multi-sweep protocol on a radial AC power network case study.

Keywords: Decentralized optimization; Tree graph; Dynamic programming (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10957-021-01828-9

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