Automatically improving constraint models in Savile Row

When solving a combinatorial problem using Constraint Programming (CP) or Satisfiability (SAT), modelling and formulation are vital and difficult tasks. Even an expert human may explore many alternatives in modelling a single problem. We make a number of contributions in the automated modelling and...

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Vydané v:Artificial intelligence Ročník 251; s. 35 - 61
Hlavní autori: Nightingale, Peter, Akgün, Özgür, Gent, Ian P., Jefferson, Christopher, Miguel, Ian, Spracklen, Patrick
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.10.2017
Elsevier Science Ltd
Predmet:
Combinatorial analysis
Common subexpression elimination
Commutativity
Computer programming
Constraint modelling
Constraint satisfaction
Modelling
Problem solving
Propositional satisfiability
Reformulation
Theory of constraints
Constraint satisfaction
Common subexpression elimination
Modelling
Propositional satisfiability
Reformulation
ISSN:0004-3702, 1872-7921
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Shrnutí:When solving a combinatorial problem using Constraint Programming (CP) or Satisfiability (SAT), modelling and formulation are vital and difficult tasks. Even an expert human may explore many alternatives in modelling a single problem. We make a number of contributions in the automated modelling and reformulation of constraint models. We study a range of automated reformulation techniques, finding combinations of techniques which perform particularly well together. We introduce and describe in detail a new algorithm, X-CSE, to perform Associative–Commutative Common Subexpression Elimination (AC-CSE) in constraint problems, significantly improving existing CSE techniques for associative and commutative operators such as +. We demonstrate that these reformulation techniques can be integrated in a single automated constraint modelling tool, called Savile Row, whose architecture we describe. We use Savile Row as an experimental testbed to evaluate each reformulation on a set of 50 problem classes, with 596 instances in total. Our recommended reformulations are well worthwhile even including overheads, especially on harder instances where solver time dominates. With a SAT solver we observed a geometric mean of 2.15 times speedup compared to a straightforward tailored model without recommended reformulations. Using a CP solver, we obtained a geometric mean of 5.96 times speedup for instances taking over 10 seconds to solve.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2017.07.001