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On Optimization Modulo Theories, MaxSMT and Sorting Networks

  • Roberto Sebastiani
  • Patrick TrentinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10206)

Abstract

Optimization Modulo Theories (\(\text {OMT}\)) is an extension of SMT which allows for finding models that optimize given objectives. (Partial weighted) MaxSMT–or equivalently \(\text {OMT}\) with Pseudo-Boolean objective functions, \(\text {OMT+PB}\) – is a very-relevant strict subcase of \(\text {OMT}\). We classify existing approaches for MaxSMT or \(\text {OMT+PB}\) in two groups: MaxSAT-based approaches exploit the efficiency of state-of-the-art MaxSAT solvers, but they are specific-purpose and not always applicable; OMT-based approaches are general-purpose, but they suffer from intrinsic inefficiencies on MaxSMT/\(\text {OMT+PB}\) problems.

We identify a major source of such inefficiencies, and we address it by enhancing \(\text {OMT}\) by means of bidirectional sorting networks. We implemented this idea on top of the OptiMathSAT \(\text {OMT}\) solver. We run an extensive empirical evaluation on a variety of problems, comparing MaxSAT-based and \(\text {OMT}\)-based techniques, with and without sorting networks, implemented on top of OptiMathSAT and Open image in new window . The results support the effectiveness of this idea, and provide interesting insights about the different approaches.

Keywords

Truth Assignment Satisfiability Modulo Theory Sequential Counter Unit Clause Satisfiability Modulo Theory Solver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.DISIUniversity of TrentoTrentoItaly

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