License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SAT.2022.25
URN: urn:nbn:de:0030-drops-166996
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16699/
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Alòs, Josep ; Ansótegui, Carlos ; Salvia, Josep M. ; Torres, Eduard

OptiLog V2: Model, Solve, Tune and Run

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LIPIcs-SAT-2022-25.pdf (0.8 MB)

Abstract

We present an extension of the OptiLog Python framework. We fully redesign the solvers module to support the dynamic loading of incremental SAT solvers with support for external libraries. We introduce new modules for modelling problems into Non-CNF format with support for Pseudo Boolean constraints, for evaluating and parsing the results of applications, and we add support for constrained execution of blackbox programs and SAT-heritage integration. All these enhancements allow OptiLog to become a swiss knife for SAT-based applications in academic and industrial environments.

BibTeX - Entry

@InProceedings{alos_et_al:LIPIcs.SAT.2022.25,
  author =	{Al\`{o}s, Josep and Ans\'{o}tegui, Carlos and Salvia, Josep M. and Torres, Eduard},
  title =	{{OptiLog V2: Model, Solve, Tune and Run}},
  booktitle =	{25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)},
  pages =	{25:1--25:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-242-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{236},
  editor =	{Meel, Kuldeep S. and Strichman, Ofer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16699},
  URN =		{urn:nbn:de:0030-drops-166996},
  doi =		{10.4230/LIPIcs.SAT.2022.25},
  annote =	{Keywords: Tool framework, Satisfiability, Modelling, Solving}
}

Keywords: Tool framework, Satisfiability, Modelling, Solving
Collection: 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)
Issue Date: 2022
Date of publication: 28.07.2022
Supplementary Material: Software (OptiLog at PyPI): https://pypi.org/project/optilog/

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