Extending Compact-Table to Basic Smart Tables

Verhaeghe, Hélène; Lecoutre, Christophe; Deville, Yves; Schaus, Pierre

doi:10.1007/978-3-319-66158-2_19

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…When the symbol '*' is present, it means that any value from the domain of the corresponding variable can be present at its position. For more information about short tables, see e.g., [23,38]. Remember that the symbol | can be used in Python to perform the union of two sets, and that we use the notation o[:, k] to extract the kth column of the array o, as in NumPy.…”

Section: Satisfy ( ( V [ I ] P [ I ] V [( I + 1) % 4] P [( I + 1) ...mentioning

confidence: 99%

PyCSP3: Modeling Combinatorial Constrained Problems in Python

Lecoutre¹,

Szczepanski²

2020

Preprint

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In this document, we introduce PyCSP 3 , a Python library that allows us to write models of combinatorial constrained problems in a simple and declarative way. Currently, with PyCSP 3 , you can write models of constraint satisfaction and optimization problems. More specifically, you can build CSP (Constraint Satisfaction Problem) and COP (Constraint Optimization Problem) models. Importantly, there is a complete separation between modeling and solving phases: you write a model, you compile it (while providing some data) in order to generate an XCSP 3 instance (file), and you solve that problem instance by means of a constraint solver. In this document, you will find all that you need to know about PyCSP 3 , with more than 40 illustrative models.

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Section: Satisfy ( ( V [ I ] P [ I ] V [( I + 1) % 4] P [( I + 1) ...mentioning

confidence: 99%

PyCSP3: Modeling Combinatorial Constrained Problems in Python

Lecoutre¹,

Szczepanski²

2020

Preprint

Self Cite

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“…Some algorithms for non-binary extensional constraints are GAC-schema [22], GAC-nextIn [86], GAC-nextDiff [59], GAC-va [82], STR2 [77], AC5TC-Tr [89], STR3 [80] and GAC4r [98]. The state-of-the-art algorithm is CT (Compact-Table ), as described in [45,139]; a filtering algorithm based on similar principles has been indepdently proposed in [143].…”

Section: Constraint Extensionmentioning

confidence: 99%

“…• acyclic smart constraints [90] • basic smart constraints [138] It is also possible to transform ordinary tables into smart tables [35]. Smart tables are very useful for modeling, permitting compact and structured representation of constraints.…”

Section: Constraint Smartmentioning

confidence: 99%

XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems

Boussemart¹,

Lecoutre²,

Audemard³

et al. 2016

Preprint

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We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups of constraints. The number of constraints is kept under control by introducing a limited set of basic constraint forms, and producing almost automatically some of their variations through lifting, restriction, sliding, logical combination and relaxation mechanisms. As a result, XCSP3 encompasses practically all constraints that can be found in major constraint solvers developed by the CP community. A website, which is developed conjointly with the format, contains many models and series of instances. The user can make sophisticated queries for selecting instances from very precise criteria. The objective of XCSP3 is to ease the effort required to test and compare different algorithms by providing a common test-bed of combinatorial constrained instances. Generic Constraints Constraint intension Constraint extension Constraint smart Language-based Constraints Constraint regular Constraint grammar Constraint mdd Comparison-based Constraints Constraints allDifferent, allEqual Constraints allDistant, ordered Counting Constraints Constraints sum, count Constraints nValues, cardinality

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“…For decades, many filtering algorithms have been proposed for enforcing Generalized Arc Consistency (GAC) on table constraints [7]- [12]. Among them, STRbit [11], Compact-Table [12] and its extensions [13]- [15] are considered to be the state-of-the-art algorithms. In modern constraint solver, enforcing GAC on a constraint model is a schedule scheme for the filter function of propagators.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Thread Parallel Constraint Propagation Scheme

Zhe

et al. 2019

IEEE Access

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Constraint Programming (CP) is an efficient technique for solving combinatorial (optimization) problems. In modern constraint solver, a CP Model is defined over reversible variables that take values in domains and propagators which filter the domains of the variables. Constraint propagation scheme schedules the propagators. A reasonable constraint propagation algorithm can improve the efficiency of solving CP problems. In this paper, we propose two efficient parallel propagation schemes based on multi-thread technique for table constraint. First, we give the formal definition of the parallel consistency and prove that the parallel propagation scheme is equivalent to the classic serial propagation scheme. Then, we propose two parallel propagation schemes: static submission and dynamic submission, which exploit work stealing thread pool and atomic operations to parallelize the classic propagation of table constraint. Finally, extensive experiments on various types of problems show that the two parallel schemes outperform their original serial version on a large number of instances. The results demonstrate the competitiveness of parallel propagation algorithms on solving extensional constraints. INDEX TERMS Constraint programming, constraint satisfaction problem, parallel constraint propagation, parallel generalized arc consistency, simple tabular reduction.

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Extending Compact-Table to Basic Smart Tables

Cited by 14 publications

References 17 publications

PyCSP3: Modeling Combinatorial Constrained Problems in Python

PyCSP3: Modeling Combinatorial Constrained Problems in Python

XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems

A Novel Multi-Thread Parallel Constraint Propagation Scheme

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