Automated streamliner portfolios for constraint satisfaction problems
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Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial problems. Solving a problem proceeds in two distinct phases: modelling and solving. Effective modelling has a huge impact on the performance of the solving process. Even with the advance of modern automated modelling tools, search spaces involved can be so vast that problems can still be difficult to solve. To further constrain the model, a more aggressive step that can be taken is the addition of streamliner constraints, which are not guaranteed to be sound but are designed to focus effort on a highly restricted but promising portion of the search space. Previously, producing effective streamlined models was a manual, difficult and time-consuming task. This paper presents a completely automated process to the generation, search and selection of streamliner portfolios to produce a substantial reduction in search effort across a diverse range of problems. The results demonstrate a marked improvement in performance for both Chuffed, a CP solver with clause learning, and lingeling, a modern SAT solver.
Spracklen , J L P J , Dang , N , Akgun , O & Miguel , I J 2023 , ' Automated streamliner portfolios for constraint satisfaction problems ' , Artificial Intelligence , vol. 319 , 103915 . https://doi.org/10.1016/j.artint.2023.103915
Copyright © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
DescriptionFunding: This work is supported by the EPSRC grants EP/P015638/1 and EP/P026842/1, and Nguyen Dang is a Leverhulme Early Career Fellow. The authors used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).
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