Automatically improving constraint models in Savile Row
| Autor: | Peter Nightingale, Ian Miguel, Özgür Akgün, Patrick Spracklen, Ian P. Gent, Christopher Jefferson |
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| Přispěvatelé: | EPSRC, The Royal Society, University of St Andrews. School of Computer Science, University of St Andrews. Centre for Interdisciplinary Research in Computational Algebra |
| Rok vydání: | 2017 |
| Předmět: |
QA75
Linguistics and Language Speedup Theoretical computer science QA75 Electronic computers. Computer science NDAS 02 engineering and technology Constraint satisfaction Modelling Language and Linguistics QA76 Set (abstract data type) QA76 Computer software Common subexpression elimination Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Constraint programming R2C Mathematics 060201 languages & linguistics Propositional satisfiability ~DC~ 06 humanities and the arts Solver Constraint (information theory) Reformulation 0602 languages and literature 020201 artificial intelligence & image processing BDC Boolean satisfiability problem Algorithm |
| Zdroj: | Artificial Intelligence. 251:35-61 |
| ISSN: | 0004-3702 |
| DOI: | 10.1016/j.artint.2017.07.001 |
| Popis: | Authors thank the EPSRC for funding this work through grants EP/H004092/1, EP/K015745/1, EP/M003728/1, and EP/P015638/1. In addition, Dr Jefferson is funded by a Royal Society University Research Fellowship. 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. Postprint |
| Databáze: | OpenAIRE |
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