Random constraint satisfaction: Easy generation of hard (satisfiable) instances

Xu, Ke; Boussemart, Frédéric; Hémery, Fred; Lecoutre, Christophe

doi:10.1016/j.artint.2007.04.001

Cited by 141 publications

(91 citation statements)

References 30 publications

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“…Random Instances These instances contain random table constraints of random scope generated by the RD-model [22]. Parameters are chosen to generate instances close to the phase transition, using Theorems 1 and 2 from [22].…”

Section: Resultsmentioning

confidence: 99%

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An Optimal Filtering Algorithm for Table Constraints

Mairy

Hentenryck

Deville

2012

Lecture Notes in Computer Science

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Abstract. Filtering algorithms for table constraints are constraint-based, which means that the propagation queue only contains information on the constraints that must be reconsidered. This paper proposes four efficient value-based algorithms for table constraints, meaning that the propagation queue also contains information on the removed values. One of these algorithms (AC5TC-Tr) is proved to have an optimal time complexity of O(r.t + r.d) per table constraint. Experimental results show that, on structured instances, all our algorithms are two or three times faster than the state of the art STR2+ and MDD c algorithms.

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Section: Resultsmentioning

confidence: 99%

“…Parameters are chosen to generate instances close to the phase transition, using Theorems 1 and 2 from [22]. The instances have 10 variables, a uniform domain size of 10, and 15 table constraints of arity 5.…”

Section: Resultsmentioning

confidence: 99%

An Optimal Filtering Algorithm for Table Constraints

Mairy

Hentenryck

Deville

2012

Lecture Notes in Computer Science

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“…According to [35], we started from densities that permit to avoid flawed instances. We consider instances involving 30 and 40 variables having a uniform domain size of 25 and 20.…”

Section: Random Binary Cspsmentioning

confidence: 99%

Weight-based Heuristics for Constraint Satisfaction and Combinatorial Optimization Problems

Huguet¹,

López²,

Karoui³

2012

J Math Model Algor

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In this paper, we propose mechanisms to improve instantiation heuristics by incorporating weighted factors on variables. The proposed weight-based heuristics are evaluated on several tree search methods such as chronological backtracking and discrepancy-based search for both constraint satisfaction and optimization problems. Experiments are carried out on random constraint satisfaction problems, car sequencing problems, and jobshop scheduling with time-lags, considering various parameter settings and variants of the methods. The results show that weighting mechanisms reduce the tree size and then speed up the solving time, especially for the discrepancy-based search method.

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“…We carry out experiments to compare CC 2 FS with five state-of-the-art MWDS algorithms on benchmarks in the literatures including unit disk graphs and random generated instances, as well as two classical graphs benchmarks namely BHOSLIB [Xu et al, 2007] and DIMACS [Johnson and Trick, 1996], and a broad range of real world massive graphs with millions of vertices and dozens of millions of edges [Rossi and Ahmed, 2015]. Experimental results show that CC 2 FS significantly outperforms previous algorithms and improves the best known solution quality for some difficult instances.…”

Section: Introductionmentioning

confidence: 99%

Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function (Extended Abstract)

Wang

Yin

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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The Minimum Weight Dominating Set (MWDS) problem is an important generalization of the Minimum Dominating Set (MDS) problem with extensive applications. This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas. The first idea is a heuristic called two-level configuration checking (CC 2 ), which is a new variant of a recent powerful configuration checking strategy (CC) for effectively avoiding the recent search paths. The second idea is a novel scoring function based on the frequency of being uncovered of vertices. Our algorithm is called CC 2 FS, according to the names of the two ideas. The experimental results show that, CC 2 FS performs much better than some state-of-the-art algorithms in terms of solution quality on a broad range of MWDS benchmarks. IntroductionGiven an undirected graph G, a dominating set D is a subset of vertices such that every vertex not in D is adjacent to at least one member of D. The Minimum Dominating Set (MDS) problem consists in identifying the smallest dominating set in a graph. The Minimum Weight Dominating Set (MWDS) problem is a generalized version of MD-S. In the MWDS problem, each vertex is associated with a positive value as its weight, and the task is to find a dominating set that minimizes the total weight of the vertices in it. The MWDS problem has played a prominent role in various real-world domains [Shen and Li, 2010;Golovach et al., 2013], such as social networks, communication networks, and industrial applications.Most practical algorithms for solving the MWDS problem are heuristic algorithms [Jovanovic et al., 2010;Potluri and Singh, 2013;Nitash and Singh, 2014; Chaurasia and Singh, 2015;Bouamama and Blum, 2016]. However, the efficiency of existing heuristic algorithm are still not satisfactory, especially for hard and large-scaled instances (as will be shown in our experiments). The reason may be that the heuristic * This paper is an extended abstract of an article in the Journal of Artificial Intelligence Research [Wang et al., 2017]

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Random constraint satisfaction: Easy generation of hard (satisfiable) instances

Cited by 141 publications

References 30 publications

An Optimal Filtering Algorithm for Table Constraints

An Optimal Filtering Algorithm for Table Constraints

Weight-based Heuristics for Constraint Satisfaction and Combinatorial Optimization Problems

Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function (Extended Abstract)

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