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GAC for a Linear Inequality and an Atleast Constraint with an Application to Learning Simple Polynomials
RISE, Swedish ICT, SICS, Computer Systems Laboratory.ORCID iD: 0000-0003-3079-8095
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We provide a filtering algorithm achieving GAC for the conjunction of constraints AtLeast(b,[x[0],x[1],...,x[n-1]],V) /\ Sum(i in 0..n-1)(a[i] c[i]) <= c where the AtLeast constraint enforces at least b variables out of x[0], x[1], ..., x[n-1] to be assigned a value in the set V. This work was motivated by learning simple polynomials, i.e. finding the coefficients of polynomials in several variables from example parameter and function values. We additionally require that coefficients be integers, and that most coefficients be assigned to zero or integers close to 0. These problems occur in the context of learning constraint models from sample solutions of different sizes. Experiments with this more global filtering show an improvement by several orders of magnitude compared to handling the constraints in isolation or with CostGCC, while also out-performing a 0/1 MIP model of the problem.

Place, publisher, year, edition, pages
2013, 6.
Keywords [en]
Constraints, Learning, Filtering Algorithms
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-24331OAI: oai:DiVA.org:ri-24331DiVA, id: diva2:1043411
Conference
Symposium on Combinatorial Search, SOCS 2013
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-24Bibliographically approved

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Carlsson, Mats

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