The Smart Table Constraint

Mairy, Jean-Baptiste; Deville, Yves; Lecoutre, Christopthe

doi:10.1007/978-3-319-18008-3_19

Cited by 22 publications

(15 citation statements)

References 20 publications

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“…For example, a regular constraint is simply a DFA defining the set of tuples which can be generated by the automata over the variables in the constraint. Similarly, (Gharbi et al 2014), and smarttables (Mairy, Deville, and Lecoutre 2015). For example, Figure 2…”

Section: Preliminariesmentioning

confidence: 99%

“…The intuition behind employing compact representations is that significant compression of tables should reduce running time for enforcing GAC. Thus besides MDD and bitwise based algorithms, more compact representations were proposed to revise existing GAC algorithms, such as the ctuples, short-supports, slice-tables, and smart-tables (Mairy, Deville, and Lecoutre 2015). The corresponding GAC algorithms of different compact representations includes: GACctuple (Katsirelos and Walsh 2007) (Mairy, Deville, and Lecoutre 2015) and smartCT for smart-tables.…”

Section: Other Representationsmentioning

confidence: 99%

See 1 more Smart Citation

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Yap

Xia

Wang

2020

AAAI

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Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.

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Section: Preliminariesmentioning

confidence: 99%

Section: Other Representationsmentioning

confidence: 99%

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Yap

Xia

Wang

2020

AAAI

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“…Some are based on using particular data structures, like Multi-valued Decision Diagrams (MDDs) [4,24], tries [7] and Deterministic Finite Automata (DFA) [25]. Other approaches attempt to keep a table-like structure, which is made compact by reasoning on Cartesian products and some intentional forms of column restrictions, like short tuples [10], compressed tuples [11,32], sliced tables [8] and smart tables [21].…”

Section: Introductionmentioning

confidence: 99%

“…We only deal with positive forms of table constraints in this paper 2. For simplicity, we consider here a slightly simpler form of smart table constraints than in[21].…”

mentioning

confidence: 99%

Extending Compact-Table to Basic Smart Tables

Verhaeghe¹,

Lecoutre

Deville³

et al. 2017

Lecture Notes in Computer Science

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Table constraints are instrumental in modelling combinatorial problems with Constraint Programming. Recently, Compact-Table (CT) has been proposed and shown to be as an efficient filtering algorithm for table constraints, notably because of bitwise operations. CT has already been extended to handle non-ordinary tables, namely, short tables and/or negative tables. In this paper, we introduce another extension so as to deal with basic smart tables, which are tables containing universal values ( * ), restrictions on values ( = v) bounds (≤ v or ≥ v) and sets (∈ S). Such tables offer the user a better expressiveness and permit to deal efficiently with compressed tuples. Our experiments show a substantial speedup when compression is possible (and a very limited overhead otherwise).

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“…Short supports represent one method of compressing table constraints, and other methods have been proposed: MDDs [7], C-tuples [13] and their generalisation [22], and Smart Tables [14]. Uniquely, short supports allow us to directly improve the encoding of Bacchus without introducing any complications to it.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Short Supports for Improved Encoding of Arbitrary Constraints into SAT

Akgün

Gent

Jefferson

et al. 2016

Lecture Notes in Computer Science

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Abstract. Encoding to SAT and applying a highly efficient modern SAT solver is an increasingly popular method of solving finite-domain constraint problems. In this paper we study encodings of arbitrary constraints where unit propagation on the encoding provides strong reasoning. Specifically, unit propagation on the encoding simulates generalised arc consistency on the original constraint. To create compact and efficient encodings we use the concept of short support. Short support has been successfully applied to create efficient propagation algorithms for arbitrary constraints. A short support of a constraint is similar to a satisfying tuple however a short support is not required to assign every variable in scope. Some variables are left free to take any value. In some cases a short support representation is smaller than the table of satisfying tuples by an exponential factor. We present two encodings based on short supports and evaluate them on a set of benchmark problems, demonstrating a substantial improvement over the state of the art.

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The Smart Table Constraint

Cited by 22 publications

References 20 publications

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Extending Compact-Table to Basic Smart Tables

Exploiting Short Supports for Improved Encoding of Arbitrary Constraints into SAT

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