A Multi-stage Graph Decomposition Algorithm for Distributed Constraint Optimisation

In this paper, we propose a novel approach to solving the distributed constraint optimisation problem (DCOP) that guarantees completeness, while having linear communication complexity. The key to performance advantages, in terms of both computation and communication, derives from the application of...

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Published in:2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology : proceedings : 18-22 December, 2006, Hong Kong, China pp. 506 - 513
Main Authors: Law, Terence H. -W., Pearce, Adrian R.
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 18.12.2006
IEEE
Series:ACM Conferences
Subjects:
Application software
Complexity theory
Computer science
Constraint optimization
Costs
Distributed computing
Laboratories
Mathematics of computing > Discrete mathematics > Combinatorics > Combinatorial algorithms
Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
Mathematics of computing > Discrete mathematics > Graph theory > Graph enumeration
Mathematics of computing > Mathematical analysis > Mathematical optimization
Problem-solving
Quality management
Software engineering
Theory of computation > Design and analysis of algorithms > Mathematical optimization
Theory of computation > Randomness, geometry and discrete structures
ISBN:9780769527482, 0769527485
Online Access:Get full text
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Abstract In this paper, we propose a novel approach to solving the distributed constraint optimisation problem (DCOP) that guarantees completeness, while having linear communication complexity. The key to performance advantages, in terms of both computation and communication, derives from the application of the repeatedly-half principle to manage complexity by a combination of problem distribution through graph decomposition and multi-stage solution quality propagation. Experimental result shows that our new algorithm is faster than a recent competitive distributed algorithm for solving MaxSAT graph colouring problems. It also indicates the potential for the decomposition approach over a centralised method based on the same search strategy, and is consistent with recent results on domain propagation and structural decomposition in the CSP literature.
AbstractList In this paper, we propose a novel approach to solving the distributed constraint optimisation problem (DCOP) that guarantees completeness, while having linear communication complexity. The key to performance advantages, in terms of both computation and communication, derives from the application of the repeatedly-half principle to manage complexity by a combination of problem distribution through graph decomposition and multi-stage solution quality propagation. Experimental result shows that our new algorithm is faster than a competitive distributed algorithm for solving MaxSAT graph colouring problems. It also indicates the potential for the decomposition approach over a centralised method based on the same search strategy, and is consistent with results on domain propagation and structural decomposition in the CSP literature.
In this paper, we propose a novel approach to solving the distributed constraint optimisation problem (DCOP) that guarantees completeness, while having linear communication complexity. The key to performance advantages, in terms of both computation and communication, derives from the application of the repeatedly-half principle to manage complexity by a combination of problem distribution through graph decomposition and multi-stage solution quality propagation. Experimental result shows that our new algorithm is faster than a recent competitive distributed algorithm for solving MaxSAT graph colouring problems. It also indicates the potential for the decomposition approach over a centralised method based on the same search strategy, and is consistent with recent results on domain propagation and structural decomposition in the CSP literature.
Author Pearce, Adrian R.
Law, Terence H. -W.
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  givenname: Adrian R.
  surname: Pearce
  fullname: Pearce, Adrian R.
  organization: The University of Melbourne, Australia
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Snippet In this paper, we propose a novel approach to solving the distributed constraint optimisation problem (DCOP) that guarantees completeness, while having linear...
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StartPage 506
SubjectTerms Application software
Complexity theory
Computer science
Constraint optimization
Costs
Distributed computing
Laboratories
Mathematics of computing -- Discrete mathematics -- Combinatorics -- Combinatorial algorithms
Mathematics of computing -- Discrete mathematics -- Graph theory -- Graph algorithms
Mathematics of computing -- Discrete mathematics -- Graph theory -- Graph enumeration
Mathematics of computing -- Mathematical analysis -- Mathematical optimization
Problem-solving
Quality management
Software engineering
Theory of computation -- Design and analysis of algorithms -- Mathematical optimization
Theory of computation -- Randomness, geometry and discrete structures
Title A Multi-stage Graph Decomposition Algorithm for Distributed Constraint Optimisation
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