Random constraint satisfaction: Easy generation of hard (satisfiable) instances
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A formal analysis shows that it is possible to generate forced satisfiable instances whose hardness is similar to unforced satisfiable ones.About:
This article is published in Artificial Intelligence.The article was published on 2007-06-01 and is currently open access. It has received 180 citations till now. The article focuses on the topics: Constraint satisfaction problem & Constraint satisfaction.read more
Citations
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Proceedings Article
Combinatorial optimization with graph convolutional networks and guided tree search
TL;DR: Experimental results demonstrate that the presented approach substantially outperforms recent deep learning work, and performs on par with highly optimized state-of-the-art heuristic solvers for some NP-hard problems.
Journal ArticleDOI
A review on algorithms for maximum clique problems
Qinghua Wu,Jin-Kao Hao +1 more
TL;DR: This review provides an updated and comprehensive review on both exact and heuristic MCP algorithms, with a special focus on recent developments, and identifies the general framework followed by these algorithms and pinpoint the key ingredients that make them successful.
Journal ArticleDOI
Online minimum matching in real-time spatial data: experiments and analysis
TL;DR: A comprehensive experimental comparison of the representative algorithms of the OMBM problem and observes a surprising result that the simple and efficient greedy algorithm, which has been considered as the worst due to its exponential worst-case competitive ratio, is significantly more effective than other algorithms.
Journal ArticleDOI
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
TL;DR: The experimental results conclude that EWLS and EWCC are largely competitive on DIMACS benchmarks, where they outperform other current best heuristic algorithms on most hard instances, and dominate on the hard random BHOSLIB benchmarks.
Book
Constraint Networks: Techniques and Algorithms
TL;DR: This book provides an accessible synthesis of the author's research and work in this area, divided into four main topics: representation, inference, search, and learning.
References
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Proceedings Article
The MD5 Message-Digest Algorithm
TL;DR: This document describes the MD5 message-digest algorithm, which takes as input a message of arbitrary length and produces as output a 128-bit "fingerprint" or "message digest" of the input.
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Consistency in Networks of Relations
TL;DR: The primary aim is to provide an accessible, unified framework, within which to present the algorithms including a new path consistency algorithm, to discuss their relationships and the may applications, both realized and potential of network consistency algorithms.
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Increasing tree search efficiency for constraint satisfaction problems
TL;DR: In this article, the authors explore the number of tree search operations required to solve binary constraint satisfaction problems and show that the two principles of first trying the places most likely to fail and remembering what has been done to avoid repeating the same mistake twice improve the standard backtracking search.
Proceedings Article
Where the really hard problems are
TL;DR: It is shown that NP-complete problems can be summarized by at least one "order parameter", and that the hard problems occur at a critical value of such a parameter.