SATzilla-07: the design and analysis of an algorithm portfolio for SAT

  • Authors:

  • Lin Xu;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

  • Affiliations:

  • University of British Columbia, Vancouver, BC, Canada;University of British Columbia, Vancouver, BC, Canada;University of British Columbia, Vancouver, BC, Canada;University of British Columbia, Vancouver, BC, Canada

  • Venue:
  • CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
  • Year:
  • 2007

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Abstract

It has been widely observed that there is no "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe a per-instance solver portfolio for SAT, SATzilla-07, which uses socalled empirical hardness models to choose among its constituent solvers. We leverage new model-building techniques such as censored sampling and hierarchical hardness models, and demonstrate the effectiveness of our techniques by building a portfolio of state-of-the-art SAT solvers and evaluating it on several widely-studied SAT data sets. Overall, we show that our portfolio significantly outperforms its constituent algorithms on every data set. Our approach has also proven itself to be effective in practice: in the 2007 SAT competition, SATzilla-07 won three gold medals, one silver, and one bronze; it is available online at http://www.cs.ubc.ca/labs/beta/Projects/SATzilla.