Fast centralized integer resource allocation algorithm and its distributed extension over digraphs

This paper studies the resource allocation problem with convex objective functions, subject to individual resource constraints, equality constraints, and integer constraints. The goal is to minimize the total cost when allocating the total resource D to n agents. We propose a novel min-heap and opti...

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Bibliographic Details
Published in:Neurocomputing Vol. 270; pp. 91 - 100
Main Authors: Xu, Yun, Yan, Gangfeng, Cai, Kai, Lin, Zhiyun
Format: Journal Article
Language:English
Japanese
Published: Elsevier B.V 27.12.2017
Elsevier BV
Subjects:
Consensus
Distributed algorithm
Min-heap
Resource allocation
Strongly connected digraph
Consensus
Strongly connected digraph
Resource allocation
Distributed algorithm
Min-heap
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:This paper studies the resource allocation problem with convex objective functions, subject to individual resource constraints, equality constraints, and integer constraints. The goal is to minimize the total cost when allocating the total resource D to n agents. We propose a novel min-heap and optimization relaxation based centralized algorithm and prove that it has a computational complexity of O(nlogn+nlogD) when the resource constraints of individual agents are [0, D], which outperforms the best known multi-phase algorithm with O(nlognlogD). By extending the centralized algorithm, we present a consensus based distributed optimization algorithm to solve the same problem. It is shown that the proposed distributed algorithm converges to a global minimizer provided that the digraph (representing the interaction topology of the agents) is strongly connected. All the updates used in the distributed algorithm rely only on local knowledge.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.03.089