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Incorporating Variance in Impact-Based Search

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Principles and Practice of Constraint Programming – CP 2011 (CP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6876))

Abstract

We present a simple modification to the idea of impact-based search which has proven highly effective for several applications. Impacts measure the average reduction in search space due to propagation after a variable assignment has been committed. Rather than considering the mean reduction only, we consider the idea of incorporating the variance in reduction. Experimental results show that using variance can result in improved search performance.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Brown University, Providence, RI, 02912, USA

    Serdar Kadioglu

  2. Dept. of Computer Science, Cornell University, Ithaca, NY, 14850, USA

    Eoin O’Mahony

  3. IBM, 1681 route des Dolines, 06560, Sophia-Antipolis, France

    Philippe Refalo

  4. IBM Watson Research Center, Yorktown Heights, NY, 10598, USA

    Meinolf Sellmann

Authors
  1. Serdar Kadioglu
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  2. Eoin O’Mahony
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  3. Philippe Refalo
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  4. Meinolf Sellmann
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China

    Jimmy Lee

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© 2011 Springer-Verlag Berlin Heidelberg

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Kadioglu, S., O’Mahony, E., Refalo, P., Sellmann, M. (2011). Incorporating Variance in Impact-Based Search. In: Lee, J. (eds) Principles and Practice of Constraint Programming – CP 2011. CP 2011. Lecture Notes in Computer Science, vol 6876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23786-7_36

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  • DOI: https://doi.org/10.1007/978-3-642-23786-7_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23785-0

  • Online ISBN: 978-3-642-23786-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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