Towards portfolios of streamlined constraint models : a case study with the balanced academic curriculum problem
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Augmenting a base constraint model with additional constraints can strengthen the inferences made by a solver and therefore reduce search effort. We focus on the automatic addition of streamliner constraints, derived from the types present in an abstract Essence specification of a problem class of interest, which trade completeness for potentially very significant reduction in search. The refinement of streamlined Essence specifications into constraint models suitable for input to constraint solvers gives rise to a large number of modelling choices in addition to those required for the base Essence specification. Previous automated streamlining approaches have been limited in evaluating only a single default model for each streamlined specification. In this paper we explore the effect of model selection in the context of streamlined specifications. We propose a new best-first search method that generates a portfolio of Pareto Optimal streamliner-model combinations by evaluating for each streamliner a portfolio of models to search and explore the variability in performance and find the optimal model. Various forms of racing are utilised to constrain the computational cost of training.
Spracklen , P , Dang , N , Akgün , Ö & Miguel , I 2020 , Towards portfolios of streamlined constraint models : a case study with the balanced academic curriculum problem . in ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation . The 19th workshop on Constraint Modelling and Reformulation (ModRef) , Louvain-la-Neuve , Belgium , 7/09/20 . < https://arxiv.org/abs/2009.10152 >workshop
ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation
Copyright © 2021by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecom-mons.org/licenses/by/4.0/).
DescriptionFunding: This work is supported by EPSRC grant EP/P015638/1 and 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). Nguyen Dang is a Leverhulme Early Career Fellow.
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