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Documents authored by Banbara, Mutsunori

Document
DOI: 10.4230/LIPIcs.SAT.2025.24
SAT-Based CEGAR Method for the Hamiltonian Cycle Problem Enhanced by Cut-Set Constraints

Authors: Ryoga Ohashi, Takehide Soh, Daniel Le Berre, Hidetomo Nabeshima, Mutsunori Banbara, Katsumi Inoue, and Naoyuki Tamura

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
In this paper, we propose an enhancement to the SAT-based counterexample-guided abstraction refinement (CEGAR) approach for solving the Hamiltonian Cycle Problem (HCP). Many SAT-based methods for HCP have been proposed, including a CEGAR-based method that repeatedly solves a relaxed version of HCP strengthened by counterexamples. However, when the counterexample space - represented by the full set of subcycle partitions - is large, it becomes difficult to find a solution. To address this, we introduce cut-set constraints in the refinement step, replacing traditional subcycle blocking constraints. Our evaluation shows that these cut-set constraints achieve equal or better reduction in the counterexample space, making it easier to find valid solutions. We further assessed performance using all 1001 instances from the FHCP challenge set and confirmed that the proposed method solved 937 instances within 1800 seconds, outperforming both the existing eager and CEGAR encodings (which solved at most 666 instances). This demonstrates the effectiveness of incorporating cut-set constraints into SAT-based CEGAR approaches.
Cite as

Ryoga Ohashi, Takehide Soh, Daniel Le Berre, Hidetomo Nabeshima, Mutsunori Banbara, Katsumi Inoue, and Naoyuki Tamura. SAT-Based CEGAR Method for the Hamiltonian Cycle Problem Enhanced by Cut-Set Constraints. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 24:1-24:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ohashi_et_al:LIPIcs.SAT.2025.24,
  author =	{Ohashi, Ryoga and Soh, Takehide and Le Berre, Daniel and Nabeshima, Hidetomo and Banbara, Mutsunori and Inoue, Katsumi and Tamura, Naoyuki},
  title =	{{SAT-Based CEGAR Method for the Hamiltonian Cycle Problem Enhanced by Cut-Set Constraints}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{24:1--24:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.24},
  URN =		{urn:nbn:de:0030-drops-237585},
  doi =		{10.4230/LIPIcs.SAT.2025.24},
  annote =	{Keywords: Hamiltonian Cycle Problem, SAT Encoding, CEGAR}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2012.86
Generating Event-Sequence Test Cases by Answer Set Programming with the Incidence Matrix

Authors: Mutsunori Banbara, Naoyuki Tamura, and Katsumi Inoue

Published in: LIPIcs, Volume 17, Technical Communications of the 28th International Conference on Logic Programming (ICLP'12) (2012)

Abstract
The effective use of ASP solvers is essential for enhancing efficiency and scalability. The incidence matrix is a simple representation used in Constraint Programming (CP) and Integer Linear Programming for modeling combinatorial problems. Generating test cases for event-sequence testing is to find a sequence covering array (SCA). In this paper, we consider the problem of finding optimal sequence covering arrays by ASP and CP. Our approach is based on an effective combination of ASP solvers and the incidence matrix. We first present three CP models from different viewpoints of sequence covering arrays: the naïve matrix model, the event-position matrix model, and the incidence matrix model. Particularly, in the incidence matrix model, an SCA can be represented by a (0,1)-matrix called the incidence matrix of the array in which the coverage constraints of the given SCA can be concisely expressed. We then present an ASP program of the incidence matrix model. It is compact and faithfully reflects the original constraints of the incidence matrix model. In our experiments, we were able to significantly improve the previously known bounds for many arrays of strength three. Moreover, we succeeded either in finding optimal solutions or in improving known bounds for some arrays of strength four.
Cite as

Mutsunori Banbara, Naoyuki Tamura, and Katsumi Inoue. Generating Event-Sequence Test Cases by Answer Set Programming with the Incidence Matrix. In Technical Communications of the 28th International Conference on Logic Programming (ICLP'12). Leibniz International Proceedings in Informatics (LIPIcs), Volume 17, pp. 86-97, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{banbara_et_al:LIPIcs.ICLP.2012.86,
  author =	{Banbara, Mutsunori and Tamura, Naoyuki and Inoue, Katsumi},
  title =	{{Generating Event-Sequence Test Cases by Answer Set Programming with the Incidence Matrix}},
  booktitle =	{Technical Communications of the 28th International Conference on Logic Programming (ICLP'12)},
  pages =	{86--97},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-43-9},
  ISSN =	{1868-8969},
  year =	{2012},
  volume =	{17},
  editor =	{Dovier, Agostino and Santos Costa, V{\'\i}tor},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2012.86},
  URN =		{urn:nbn:de:0030-drops-36127},
  doi =		{10.4230/LIPIcs.ICLP.2012.86},
  annote =	{Keywords: Event-Sequence Testing, Answer Set Programming, Matrix Model, Constraint Programming, Propositional Satisfiability (SAT)}
}
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