SATzilla

SATzilla: portfolio-based algorithm selection for SAT. It has been widely observed that there is no single ”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 SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.


References in zbMATH (referenced in 71 articles )

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  1. Cerutti, Federico; Vallati, Mauro; Giacomin, Massimiliano: On the impact of configuration on abstract argumentation automated reasoning (2018)
  2. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  3. Gnad, Daniel; Hoffmann, Jörg: Star-topology decoupled state space search (2018)
  4. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)
  5. Olier, Ivan; Sadawi, Noureddin; Bickerton, G. Richard; Vanschoren, Joaquin; Grosan, Crina; Soldatova, Larisa; King, Ross D.: Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2018)
  6. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  7. Ansótegui, Carlos; Bonet, Maria Luisa; Giráldez-Cru, Jesús; Levy, Jordi: Structure features for SAT instances classification (2017)
  8. Demyanova, Yulia; Pani, Thomas; Veith, Helmut; Zuleger, Florian: Empirical software metrics for benchmarking of verification tools (2017)
  9. Flerova, Natalia; Marinescu, Radu; Dechter, Rina: Weighted heuristic anytime search: new schemes for optimization over graphical models (2017)
  10. Gupta, Rishi; Roughgarden, Tim: A PAC approach to application-specific algorithm selection (2017)
  11. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  12. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  13. Minot, Maël; Ndiaye, Samba Ndojh; Solnon, Christine: Combining CP and ILP in a tree decomposition of bounded height for the Sum Colouring problem (2017)
  14. Mısır, Mustafa; Sebag, Michèle: Alors: an algorithm recommender system (2017)
  15. Niemetz, Aina; Preiner, Mathias; Biere, Armin: Propagation based local search for bit-precise reasoning (2017)
  16. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Portfolio approaches for constraint optimization problems (2016)
  17. Ansótegui, Carlos; Gabàs, Joel; Malitsky, Yuri; Sellmann, Meinolf: MaxSAT by improved instance-specific algorithm configuration (2016)
  18. Audemard, Gilles; Simon, Laurent: Extreme cases in SAT problems (2016)
  19. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  20. Cauwet, Marie-Liesse; Liu, Jialin; Rozière, Baptiste; Teytaud, Olivier: Algorithm portfolios for noisy optimization (2016)

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