Optimization Modulo Theories (OMT) is an extension of SMT that allows for finding models that optimize objective functions. In this paper we aim at bridging the gap between Constraint Programming (CP) and OMT, in both directions. First, we have extended the OMT solver OptiMathSAT with a FlatZinc interface – which can also be used as FlatZinc-to-OMT encoder for other OMT solvers. This allows OMT tools to be used in combination with mzn2fzn on the large amount of CP problems coming from the MiniZinc community. Second, we have introduced a tool for translating SMT and OMT problems on the linear arithmetic and bit-vector theories into MiniZinc. This allows MiniZinc solvers to be used on a large amount of SMT/OMT problems. We have discussed the main issues we had to cope with in either directions. We have performed an extensive empirical evaluation comparing three state-of-the-art OMT-based tools with many state-of-the-art CP tools on (i) CP problems coming from the MiniZinc challenge, and ...

Optimization Modulo Theories (OMT) is an extension of SMT that allows for finding models that optimize objective functions. In this paper we aim at bridging the gap between Constraint Programming (CP) and OMT, in both directions. First, we have extended the OMT solver OptiMathSAT with a FlatZinc interface – which can also be used as FlatZinc-to-OMT encoder for other OMT solvers. This allows OMT tools to be used in combination with mzn2fzn on the large amount of CP problems coming from the MiniZinc community. Second, we have introduced a tool for translating SMT and OMT problems on the linear arithmetic and bit-vector theories into MiniZinc. This allows MiniZinc solvers to be used on a large amount of SMT/OMT problems. We have discussed the main issues we had to cope with in either directions. We have performed an extensive empirical evaluation comparing three state-of-the-art OMT-based tools with many state-of-the-art CP tools on (i) CP problems coming from the MiniZinc challenge, and (ii) OMT problems coming mostly from formal verification. This analysis also allowed us to identify some criticalities, in terms of efficiency and correctness, one has to cope with when addressing CP problems with OMT tools, and vice versa.

From MiniZinc to Optimization Modulo Theories, and Back / Contaldo, Francesco; Trentin, Patrick; Sebastiani, Roberto. - 12296:(2020), pp. 148-166. ( 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2020 Wien 21th-24th September 2020) [10.1007/978-3-030-58942-4_10].

From MiniZinc to Optimization Modulo Theories, and Back

Patrick Trentin;Roberto Sebastiani
2020-01-01

Abstract

Optimization Modulo Theories (OMT) is an extension of SMT that allows for finding models that optimize objective functions. In this paper we aim at bridging the gap between Constraint Programming (CP) and OMT, in both directions. First, we have extended the OMT solver OptiMathSAT with a FlatZinc interface – which can also be used as FlatZinc-to-OMT encoder for other OMT solvers. This allows OMT tools to be used in combination with mzn2fzn on the large amount of CP problems coming from the MiniZinc community. Second, we have introduced a tool for translating SMT and OMT problems on the linear arithmetic and bit-vector theories into MiniZinc. This allows MiniZinc solvers to be used on a large amount of SMT/OMT problems. We have discussed the main issues we had to cope with in either directions. We have performed an extensive empirical evaluation comparing three state-of-the-art OMT-based tools with many state-of-the-art CP tools on (i) CP problems coming from the MiniZinc challenge, and ...
2020
Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SPRINGER INTERNATIONAL PUBLISHING AG
978-3-030-58941-7
978-3-030-58942-4
Contaldo, Francesco; Trentin, Patrick; Sebastiani, Roberto
From MiniZinc to Optimization Modulo Theories, and Back / Contaldo, Francesco; Trentin, Patrick; Sebastiani, Roberto. - 12296:(2020), pp. 148-166. ( 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2020 Wien 21th-24th September 2020) [10.1007/978-3-030-58942-4_10].
File in questo prodotto:
File Dimensione Formato  
cpaior20.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 319.51 kB
Formato Adobe PDF
319.51 kB Adobe PDF   Visualizza/Apri
Contaldo2020_Chapter_FromMiniZincToOptimizationModu.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 591.82 kB
Formato Adobe PDF
591.82 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/287779
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
  • OpenAlex 5
social impact