Adaptive Step Size for a Consensus based Distributed Subgradient Method in Generalized Mutual Assignment Problem

12 pages•Published: September 20, 2022

Yuki Amemiya, Kenta Hanada and Kenji Sugimoto

Abstract

Generalized Mutual Assignment Problem (GMAP) is a multi-agent based distributed optimization where the agents try to obtain the most profitable job assignment. Since it is NP-hard and even a problem of judging the existence of a feasible solution is NP-complete, it is a challenging issue to solve GMAP. In this paper, a consensus based distributed subgradient method is considered to obtain feasible solutions of GMAP as good as possible. Adaptive step size which is calculated by the lower and estimated upper bounds is proposed for the step size in the subgradient method. In addition, a protocol how to estimate the upper bound is also proposed, where each agent do not have to synchronize it.

Keyphrases: adaptive step size, assignment problem, distributed optimization, heuristic algorithms, subgradient method

In: Tokuro Matsuo (editor). Proceedings of 11th International Congress on Advanced Applied Informatics, vol 81, pages 141-152.

Links:	https://easychair.org/publications/paper/Xgpg
	https://doi.org/10.29007/k1bg

BibTeX entry

@inproceedings{IIAIAAI2021-Winter:Adaptive_Step_Size_Consensus,
  author    = {Yuki Amemiya and Kenta Hanada and Kenji Sugimoto},
  title     = {Adaptive Step Size for a Consensus based Distributed Subgradient Method in Generalized Mutual Assignment Problem},
  booktitle = {Proceedings of 11th International Congress on Advanced Applied Informatics},
  editor    = {Tokuro Matsuo},
  series    = {EPiC Series in Computing},
  volume    = {81},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/Xgpg},
  doi       = {10.29007/k1bg},
  pages     = {141-152},
  year      = {2022}}

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