New algorithms for max restricted path consistency

Balafoutis, Thanasis; Paparrizou, Anastasia; Stergiou, Kostas; Walsh, Toby

doi:10.1007/s10601-011-9110-y

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…We apply p-LC to the case where LC is max restricted path consistency. We describe the algorithm p-maxRPC3 (based on maxRPC3 [1]) that achieves p-max restricted path consistency. Then, we propose ap-LC, an adaptive variant of p-LC which adapts dynamically and locally the level of local consistency during search.…”

Section: This Work Has Been Funded By the Eu Project Icon (Fp7-284715)mentioning

confidence: 99%

“…We propose an algorithm for p-maxRPC, based on maxRPC3, the best existing maxRPC algoritm. We do not describe maxRPC3 in full detail as it can be found in [1]. We only describe procedures where changes to maxRPC3 are necessary to design p-maxRPC3, a coarse grained algorithm that performs pmaxRPC.…”

Section: Definition 2 (P-stability For Ac) a Valuementioning

confidence: 99%

“…The purpose of this experiment is to see if there exist problems on which a given level of p-maxRPC, with a p uniform on all the constraint network and static during the whole search is more efficient than AC or maxRPC, or both. We have implemented the algorithms that achieve p-maxRPC as described in the previous section in our own binary constraint solver, in addition to maxRPC (maxRPC3 version [1]) and AC (AC2001 version [3]). All these algorithms are maintained during search.…”

Section: Experimental Validation Of P-maxrpcmentioning

confidence: 99%

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Adaptive Parameterized Consistency

Balafrej

Bessière

Coletta

et al. 2013

Lecture Notes in Computer Science

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Abstract. State-of-the-art constraint solvers uniformly maintain the same level of local consistency (usually arc consistency) on all the instances. We propose parameterized local consistency, an original approach to adjust the level of consistency depending on the instance and on which part of the instance we propagate. We do not use as parameter one of the features of the instance, as done for instance in portfolios of solvers. We use as parameter the stability of values, which is a feature based on the state of the arc consistency algorithm during its execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency on a value depending on whether the stability of the value is above or below a given threshold. We also propose a way to dynamically adapt the parameter, and thus the level of local consistency, during search. This approach allows us to get a good trade-off between the number of values pruned and the computational cost. We validate our approach on various problems from the CSP competition.

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Section: This Work Has Been Funded By the Eu Project Icon (Fp7-284715)mentioning

confidence: 99%

Section: Definition 2 (P-stability For Ac) a Valuementioning

confidence: 99%

Section: Experimental Validation Of P-maxrpcmentioning

confidence: 99%

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Adaptive Parameterized Consistency

Balafrej

Bessière

Coletta

et al. 2013

Lecture Notes in Computer Science

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“…Afterwards, Thanasis Balafoutis et al exploited ideas from ACS-resOpt algorithm in maxRPC3rm and maxRPC3 and proposed the lmaxRPC3rm-resOpt algorithm. Although the algorithm has decreased the theoretical complexity, but experimentation shows it achieve a poor performance due to complicated operations on data structures [18].…”

Section: Introductionmentioning

confidence: 99%

lmaxRPCls: An Algorithm Utilizing Light Symmetry for Approximating maxRPC in Constraint Programming

Zhang¹,

Song²,

Li³

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-Constraint satisfaction problem (CSP) can be widely applied in many areas. This paper investigates the maximum restricted path consistency algorithm. There is a large quantity of useless checks in the process of searching for a PC-support with the most popular algorithm lmaxRPC3rm. Since lmaxRPC3rm has to examine the whole domain of a variable to ascertain whether a PC-support exists. The efficiency of the search can be improved by eliminating such useless checks. Firstly, this paper analyses the features which accounts for the existence of these ineffective checks. And then, this paper discusses some methods of solving these problems. Afterwards, a new data structure is put forward to strengthen residual supports and weaken the use of multidirectionality to narrow the range of search. A new algorithm, lmaxRPCls, which exploits the results above is proposed and it is proved that lmaxRPCls is correct and complete. It is also proved that the time complexity of this new algorithm is better than that of lmaxRPC3rm. Experimental results show that lmaxRPCls performs better in most benchmark instances and it can improve the performance by 65% in the best case.

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“…We apply p-LC to the case where LC is max restricted path consistency. We describe the algorithm p-maxRPC3 (based on maxRPC3 [BPSW11]) that achieves p-max restricted path consistency. Then, we propose ap-LC, an adaptive variant of p-LC that uses the number of failures in which variables or constraints are involved to assess the di culty of the di↵erent parts of the problem during search.…”

Section: Introductionmentioning

confidence: 99%

Adapting Consistency in Constraint Solving

Balafrej

Bessière

Paparrizou

et al. 2016

Data Mining and Constraint Programming

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State-of-the-art constraint solvers uniformly maintain the same level of local consistency (usually arc consistency) on all the instances. We propose two approaches to adjust the level of consistency depending on the instance and on which part of the instance we propagate. The first approach, parameterized local consistency, uses as parameter the stability of values, which is a feature computed by arc consistency algorithms during their execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency to a value depending on whether the stability of the value is above or below a given threshold. In the adaptive version, the parameter is dynamically adapted during search, and so is the level of local consistency. In the second approach, we focus on partition-one-AC, a singleton-based consistency. We propose adaptive variants of partition-one-AC that do not necessarily run until having proved the fixpoint. The pruning can be weaker than the full version, but the computational e↵ort can be significantly reduced. Our experiments show that adaptive parameterized maxRPC and adaptive partition-one-AC can obtain significant speed-ups over arc consistency and over the full versions of maxRPC and partition-one-AC. ⇤ The results contained in this chapter have been presented in [BBCB13] and [BBBT14]. This work has been funded by the EU project ICON (FP7-284715).

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New algorithms for max restricted path consistency

Cited by 16 publications

References 16 publications

Adaptive Parameterized Consistency

Adaptive Parameterized Consistency

lmaxRPCls: An Algorithm Utilizing Light Symmetry for Approximating maxRPC in Constraint Programming

Adapting Consistency in Constraint Solving

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