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Open Source Integer Linear Programming Solvers for Error Localization in Numerical Data

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Advances in Theoretical and Applied Statistics

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

Error localization problems can be converted into Integer Linear Programming problems. This approach provides several advantages and guarantees to find a set of erroneous fields having minimum total cost. By doing so, each erroneous record produces an Integer Linear Programming model that should be solved. This requires the use of specific solution softwares called Integer Linear Programming solvers. Some of these solvers are available as open source software. A study on the performance of internationally recognized open source Integer Linear Programming solvers, compared to a reference commercial solver on real-world data having only numerical fields, is reported. The aim was to produce a stressing test environment for selecting the most appropriate open source solver for performing error localization in numerical data.

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Author information

Authors and Affiliations

  1. Istat, DCCG, Via A. Ravà 150, Roma, Italy

    Gianpiero Bianchi & Alessandra Reale

  2. Univ. Roma “Sapienza”, DIS, Via Ariosto 25, Roma, Italy

    Renato Bruni

Authors
  1. Gianpiero Bianchi
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  2. Renato Bruni
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  3. Alessandra Reale
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Corresponding author

Correspondence to Gianpiero Bianchi .

Editor information

Editors and Affiliations

  1. Department of Statistics, University of Trieste, P.le Europa 1, Trieste, 34127, Italy

    Nicola Torelli

  2. Department of Statistics, University of Padua, Via Cesare Battisti 241, Padova, 35121, Italy

    Fortunato Pesarin

  3. Université Paris Descartes, rue des Saints-Pères 45, Paris, 75270, France

    Avner Bar-Hen

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Bianchi, G., Bruni, R., Reale, A. (2013). Open Source Integer Linear Programming Solvers for Error Localization in Numerical Data. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_28

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