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DOI: 10.4230/LIPIcs.CP.2024.6

Deep Cooperation of Local Search and Unit Propagation Techniques

Authors: Xiamin Chen, Zhendong Lei, and Pinyan Lu

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract

Local search (LS) is an efficient method for solving combinatorial optimization problems such as MaxSAT and Pseudo Boolean Problems (PBO). However, due to a lack of reasoning power and global information, LS methods get stuck at local optima easily. In contrast to the LS, Systematic Search utilizes unit propagation and clause learning techniques with strong reasoning capabilities to avoid falling into local optima. Nevertheless, the complete search is generally time-consuming to obtain a global optimal solution. This work proposes a deep cooperation framework combining local search and unit propagation to address their inherent disadvantages. First, we design a mechanism to detect when LS gets stuck, and then a well-designed unit propagation procedure is called upon to help escape the local optima. To the best of our knowledge, we are the first to integrate unit propagation technique within LS to overcome local optima. Experiments based on a broad range of benchmarks from MaxSAT Evaluations, PBO competitions, the Mixed Integer Programming Library, and three real-life cases validate that our method significantly improves three state-of-the-art MaxSAT and PBO local search solvers.

Cite as

Xiamin Chen, Zhendong Lei, and Pinyan Lu. Deep Cooperation of Local Search and Unit Propagation Techniques. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{chen_et_al:LIPIcs.CP.2024.6,
  author =	{Chen, Xiamin and Lei, Zhendong and Lu, Pinyan},
  title =	{{Deep Cooperation of Local Search and Unit Propagation Techniques}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{6:1--6:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.6},
  URN =		{urn:nbn:de:0030-drops-206918},
  doi =		{10.4230/LIPIcs.CP.2024.6},
  annote =	{Keywords: PBO, Partial MaxSAT, LS, CDCL}
}

Document

DOI: 10.4230/LIPIcs.CP.2024.26

Structure-Guided Local Improvement for Maximum Satisfiability

Authors: André Schidler and Stefan Szeider

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract

The enhanced performance of today’s MaxSAT solvers has elevated their appeal for many large-scale applications, notably in software analysis and computer-aided design. Our research delves into refining anytime MaxSAT solving by repeatedly identifying and solving with an exact solver smaller subinstances that are chosen based on the graphical structure of the instance. We investigate various strategies to pinpoint these subinstances. This structure-guided selection of subinstances provides an exact solver with a high potential for improving the current solution. Our exhaustive experimental analyses contrast our methodology as instantiated in our tool MaxSLIM with previous studies and benchmark it against leading-edge MaxSAT solvers.

Cite as

André Schidler and Stefan Szeider. Structure-Guided Local Improvement for Maximum Satisfiability. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 26:1-26:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{schidler_et_al:LIPIcs.CP.2024.26,
  author =	{Schidler, Andr\'{e} and Szeider, Stefan},
  title =	{{Structure-Guided Local Improvement for Maximum Satisfiability}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{26:1--26:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.26},
  URN =		{urn:nbn:de:0030-drops-207112},
  doi =		{10.4230/LIPIcs.CP.2024.26},
  annote =	{Keywords: maximum satisfiability, large neighborhood search (LNS), SAT-based local improvement (SLIM), incomplete MaxSAT, graphical structure, metaheuristic}
}

Document

DOI: 10.4230/LIPIcs.CP.2024.31

Inverting Step-Reduced SHA-1 and MD5 by Parameterized SAT Solvers

Authors: Oleg Zaikin

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract

MD5 and SHA-1 are fundamental cryptographic hash functions proposed in 1990s. Given a message of arbitrary finite size, MD5 produces a 128-bit hash in 64 steps, while SHA-1 produces a 160-bit hash in 80 steps. It is computationally infeasible to invert MD5 and SHA-1, i.e. to find a message given a hash. In 2012, 28-step MD5 and 23-step SHA-1 were inverted by CDCL solvers, yet no progress has been made since then. The present paper proposes to construct 31 intermediate inverse problems for any pair of MD5 or SHA-1 steps (i,i+1), such that the first problem is very close to inverting i steps, while the 31st one is almost inverting i+1 steps. We constructed SAT encodings of intermediate problems for MD5 and SHA-1, and tuned a CDCL solver on the simplest of them. Then the tuned solver was used to design a parallel Cube-and-Conquer solver which for the first time inverted 29-step MD5 and 24-step SHA-1.

Cite as

Oleg Zaikin. Inverting Step-Reduced SHA-1 and MD5 by Parameterized SAT Solvers. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 31:1-31:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{zaikin:LIPIcs.CP.2024.31,
  author =	{Zaikin, Oleg},
  title =	{{Inverting Step-Reduced SHA-1 and MD5 by Parameterized SAT Solvers}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{31:1--31:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.31},
  URN =		{urn:nbn:de:0030-drops-207165},
  doi =		{10.4230/LIPIcs.CP.2024.31},
  annote =	{Keywords: cryptographic hash function, MD5, SHA-1, preimage attack, SAT, Cube-and-Conquer}
}

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Short Paper

DOI: 10.4230/LIPIcs.CP.2024.34

Minimizing Working-Group Conflicts in Conference Session Scheduling Through Maximum Satisfiability (Short Paper)

Authors: Sami Cherif, Heythem Sattoutah, Chu-Min Li, Corinne Lucet, and Laure Brisoux-Devendeville

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract

This paper explores the application of Maximum Satisfiability (Max-SAT) to the complex problem of conference session scheduling, with a particular focus on minimizing working-group conflicts within the context of the ROADEF conference, the largest French-speaking event aimed at bringing together researchers from various fields such as combinatorial optimization and operational research. A Max-SAT model is introduced then enhanced with new variables, and solved through state-of-the-art solvers. The results of applying our formulation to data from ROADEF demonstrate its ability to effectively compute session schedules, while enabling to reduce the number of conflicts and the maximum number of parallel sessions compared to the handmade solutions proposed by the organizing committees. These findings underscore the potential of Max-SAT as a valuable tool for optimizing conference scheduling processes, offering a systematic and efficient solution that ensures a smoother and more productive experience for attendees and organizers alike.

Cite as

Sami Cherif, Heythem Sattoutah, Chu-Min Li, Corinne Lucet, and Laure Brisoux-Devendeville. Minimizing Working-Group Conflicts in Conference Session Scheduling Through Maximum Satisfiability (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 34:1-34:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{cherif_et_al:LIPIcs.CP.2024.34,
  author =	{Cherif, Sami and Sattoutah, Heythem and Li, Chu-Min and Lucet, Corinne and Brisoux-Devendeville, Laure},
  title =	{{Minimizing Working-Group Conflicts in Conference Session Scheduling Through Maximum Satisfiability}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{34:1--34:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.34},
  URN =		{urn:nbn:de:0030-drops-207190},
  doi =		{10.4230/LIPIcs.CP.2024.34},
  annote =	{Keywords: Maximum Satisfiability, Scheduling, Modeling}
}

Document

Short Paper

DOI: 10.4230/LIPIcs.CP.2024.38

Frugal Algorithm Selection (Short Paper)

Authors: Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract

When solving decision and optimisation problems, many competing algorithms (model and solver choices) have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of training can be prohibitively large due to running candidate algorithms on a representative set of training instances. In this work, we explore reducing this cost by choosing a subset of the training instances on which to train. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout. We evaluate combinations of these approaches on six datasets from ASLib and present the reduction in labelling cost achieved by each option.

Cite as

Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel. Frugal Algorithm Selection (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 38:1-38:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{kus_et_al:LIPIcs.CP.2024.38,
  author =	{Ku\c{s}, Erdem and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Miguel, Ian},
  title =	{{Frugal Algorithm Selection}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{38:1--38:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.38},
  URN =		{urn:nbn:de:0030-drops-207239},
  doi =		{10.4230/LIPIcs.CP.2024.38},
  annote =	{Keywords: Algorithm Selection, Active Learning}
}

Document

DOI: 10.4230/LIPIcs.SAT.2024.8

Enhancing MaxSAT Local Search via a Unified Soft Clause Weighting Scheme

Authors: Yi Chu, Chu-Min Li, Furong Ye, and Shaowei Cai

Published in: LIPIcs, Volume 305, 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)

Abstract

Local search has been widely applied to solve the well-known (weighted) partial MaxSAT problem, significantly influencing many real-world applications. The main difficulty to overcome when designing a local search algorithm is that it can easily fall into local optima. Clause weighting is a beneficial technique that dynamically adjusts the landscape of search space to help the algorithm escape from local optima. Existing works tend to increase the weights of falsified clauses, and such strategies may result in an unpredictable landscape of search space during the optimization process. Therefore, in this paper, we propose a Unified Soft Clause Weighting Scheme called Unified-SW, which increases the weights of all soft clauses in feasible local optima, whether they are satisfied or not, while preserving the hierarchy among them. We implemented Unified-SW in a new local search solver called USW-LS. Experimental results demonstrate that USW-LS, outperforms the state-of-the-art local search solvers across benchmarks from anytime tracks of recent MaxSAT Evaluations. More promisingly, a hybrid solver combining USW-LS and TT-Open-WBO-Inc won all four categories in the anytime track of MaxSAT Evaluation 2023.

Cite as

Yi Chu, Chu-Min Li, Furong Ye, and Shaowei Cai. Enhancing MaxSAT Local Search via a Unified Soft Clause Weighting Scheme. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 8:1-8:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{chu_et_al:LIPIcs.SAT.2024.8,
  author =	{Chu, Yi and Li, Chu-Min and Ye, Furong and Cai, Shaowei},
  title =	{{Enhancing MaxSAT Local Search via a Unified Soft Clause Weighting Scheme}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{8:1--8:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.8},
  URN =		{urn:nbn:de:0030-drops-205301},
  doi =		{10.4230/LIPIcs.SAT.2024.8},
  annote =	{Keywords: Weighted Partial MaxSAT, Local Search Method, Weighting Scheme}
}

Document

DOI: 10.4230/LIPIcs.SAT.2024.12

SAT Encoding of Partial Ordering Models for Graph Coloring Problems

Authors: Daniel Faber, Adalat Jabrayilov, and Petra Mutzel

Published in: LIPIcs, Volume 305, 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)

Abstract

In this paper, we revisit SAT encodings of the partial-ordering based ILP model for the graph coloring problem (GCP) and suggest a generalization for the bandwidth coloring problem (BCP). The GCP asks for the minimum number of colors that can be assigned to the vertices of a given graph such that each two adjacent vertices get different colors. The BCP is a generalization, where each edge has a weight that enforces a minimal "distance" between the assigned colors, and the goal is to minimize the "largest" color used. For the widely studied GCP, we experimentally compare the partial-ordering based SAT encoding to the state-of-the-art approaches on the DIMACS benchmark set. Our evaluation confirms that this SAT encoding is effective for sparse graphs and even outperforms the state-of-the-art on some DIMACS instances. For the BCP, our theoretical analysis shows that the partial-ordering based SAT and ILP formulations have an asymptotically smaller size than that of the classical assignment-based model. Our practical evaluation confirms not only a dominance compared to the assignment-based encodings but also to the state-of-the-art approaches on a set of benchmark instances. Up to our knowledge, we have solved several open instances of the BCP from the literature for the first time.

Cite as

Daniel Faber, Adalat Jabrayilov, and Petra Mutzel. SAT Encoding of Partial Ordering Models for Graph Coloring Problems. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 12:1-12:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{faber_et_al:LIPIcs.SAT.2024.12,
  author =	{Faber, Daniel and Jabrayilov, Adalat and Mutzel, Petra},
  title =	{{SAT Encoding of Partial Ordering Models for Graph Coloring Problems}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{12:1--12:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.12},
  URN =		{urn:nbn:de:0030-drops-205340},
  doi =		{10.4230/LIPIcs.SAT.2024.12},
  annote =	{Keywords: Graph coloring, bandwidth coloring, SAT encodings, ILP formulations}
}

Document

DOI: 10.4230/LIPIcs.SAT.2024.16

Towards Universally Accessible SAT Technology

Authors: Alexey Ignatiev, Zi Li Tan, and Christos Karamanos

Published in: LIPIcs, Volume 305, 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)

Abstract

Boolean satisfiability (SAT) solvers are a family of highly efficient reasoning engines, which are frequently used for solving a large and diverse variety of practical challenges. This applies to multidisciplinary problems belonging to the class NP but also those arising at higher levels of the polynomial hierarchy. Unfortunately, encoding a problem of user’s interest to a (series of) propositional formula(s) in conjunctive normal form (CNF), let alone dealing with a SAT solver, is rarely a simple task even for an experienced SAT practitioner. This situation gets aggravated further when the user has little to no knowledge on the operation of the modern SAT solving technology. In 2018, the PySAT framework was proposed to address the issue of fast and "painless" prototyping with SAT solvers in Python allowing researchers to get SAT-based solutions to their problems without investing substantial time in the development process and yet sacrificing only a little in terms of performance. Since then, PySAT has proved a useful instrument for solving a wide range of practical problems and is now a critical package for the PyPI infrastructure. In the meantime, there have been advances in SAT solving and enhancements to PySAT functionality to extend its modelling and solving capabilities in order to make modern SAT technology accessible and deployable on a massive scale. This paper provides a high-level overview of the current architecture of PySAT and some of its capabilities including arbitrary Boolean formula manipulation, CNF preprocessing, and support for external user-defined propagators.

Cite as

Alexey Ignatiev, Zi Li Tan, and Christos Karamanos. Towards Universally Accessible SAT Technology. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 16:1-16:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{ignatiev_et_al:LIPIcs.SAT.2024.16,
  author =	{Ignatiev, Alexey and Tan, Zi Li and Karamanos, Christos},
  title =	{{Towards Universally Accessible SAT Technology}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{16:1--16:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.16},
  URN =		{urn:nbn:de:0030-drops-205382},
  doi =		{10.4230/LIPIcs.SAT.2024.16},
  annote =	{Keywords: PySAT, Python, Prototyping, Practical Applicability}
}

Document

DOI: 10.4230/LIPIcs.SAT.2024.17

Parallel Clause Sharing Strategy Based on Graph Structure of SAT Problem

Authors: Yoichiro Iida, Tomohiro Sonobe, and Mary Inaba

Published in: LIPIcs, Volume 305, 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)

Abstract

Parallelization of SAT solvers is an important technique for improving solver performance. The selection of the learnt clauses to share among parallel workers is crucial for its efficiency. Literal block distance (LBD) is often used to evaluate the quality of clauses to select. We propose a new method, Parallel Clause sharing based on graph Structure (PaCS), to select good clauses for sharing. First, we conducted three preliminary experiments to assess the performance of LBD in parallel clause sharing: a performance comparison between the LBD and clause size, an analysis of the utilization of shared clauses, and a comparison of the LBD values of shared clauses at originating and receiving workers. These experiments indicate that the LBD may not be optimal for learnt clause sharing. We attribute the results to the LBD’s inherent dependency on decision trees. Each parallel worker has a unique decision tree; thus, a sharing clause that is good for its originating worker may not be good for others. Therefore, we propose PaCS, a search-independent method that uses the graph structure derived from the input CNF of SAT problems. PaCS evaluates clauses using their edges' weight in the variable incidence graph. Using the input CNF’s graph is effective for parallel clause sharing because it is the common input for all parallel workers. Furthermore, using edge weight can select clauses whose variables' Boolean values are more likely to be determined. Performance evaluation experiments demonstrate that our strategy outperforms LBD by 4% in the number of solved instances and by 12% in PAR-2. This study opens avenues for further improvements in parallel-solving strategies using the structure of SAT problems and reinterpretations of the quality of learnt clauses.

Cite as

Yoichiro Iida, Tomohiro Sonobe, and Mary Inaba. Parallel Clause Sharing Strategy Based on Graph Structure of SAT Problem. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 17:1-17:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{iida_et_al:LIPIcs.SAT.2024.17,
  author =	{Iida, Yoichiro and Sonobe, Tomohiro and Inaba, Mary},
  title =	{{Parallel Clause Sharing Strategy Based on Graph Structure of SAT Problem}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{17:1--17:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.17},
  URN =		{urn:nbn:de:0030-drops-205392},
  doi =		{10.4230/LIPIcs.SAT.2024.17},
  annote =	{Keywords: SAT Solver, Structure of SAT, Parallel application, Clause Learning}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.7

Exploiting Configurations of MaxSAT Solvers

Authors: Josep Alòs, Carlos Ansótegui, Josep M. Salvia, and Eduard Torres

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

In this paper, we describe how we can effectively exploit alternative parameter configurations to a MaxSAT solver. We describe how these configurations can be computed in the context of MaxSAT. In particular, we experimentally show how to easily combine configurations of a non-competitive solver to obtain a better solving approach.

Cite as

Josep Alòs, Carlos Ansótegui, Josep M. Salvia, and Eduard Torres. Exploiting Configurations of MaxSAT Solvers. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{alos_et_al:LIPIcs.CP.2023.7,
  author =	{Al\`{o}s, Josep and Ans\'{o}tegui, Carlos and Salvia, Josep M. and Torres, Eduard},
  title =	{{Exploiting Configurations of MaxSAT Solvers}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{7:1--7:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.7},
  URN =		{urn:nbn:de:0030-drops-190443},
  doi =		{10.4230/LIPIcs.CP.2023.7},
  annote =	{Keywords: maximum satisfiability, maxsat evaluation, automatic configuration}
}

Document

DOI: 10.4230/LIPIcs.SAT.2022.25

OptiLog V2: Model, Solve, Tune and Run

Authors: Josep Alòs, Carlos Ansótegui, Josep M. Salvia, and Eduard Torres

Published in: LIPIcs, Volume 236, 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)

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.

Cite as

Josep Alòs, Carlos Ansótegui, Josep M. Salvia, and Eduard Torres. OptiLog V2: Model, Solve, Tune and Run. In 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 236, pp. 25:1-25:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@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/entities/document/10.4230/LIPIcs.SAT.2022.25},
  URN =		{urn:nbn:de:0030-drops-166996},
  doi =		{10.4230/LIPIcs.SAT.2022.25},
  annote =	{Keywords: Tool framework, Satisfiability, Modelling, Solving}
}

Document

DOI: 10.4230/LIPIcs.CP.2021.12

Building High Strength Mixed Covering Arrays with Constraints

Authors: Carlos Ansótegui, Jesús Ojeda, and Eduard Torres

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract

Covering arrays have become a key piece in Combinatorial Testing. In particular, we focus on the efficient construction of Covering Arrays with Constraints of high strength. SAT solving technology has been proven to be well suited when solving Covering Arrays with Constraints. However, the size of the SAT reformulations rapidly grows up with higher strengths. To this end, we present a new incomplete algorithm that mitigates substantially memory blow-ups. The experimental results confirm the goodness of the approach, opening avenues for new practical applications.

Cite as

Carlos Ansótegui, Jesús Ojeda, and Eduard Torres. Building High Strength Mixed Covering Arrays with Constraints. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{ansotegui_et_al:LIPIcs.CP.2021.12,
  author =	{Ans\'{o}tegui, Carlos and Ojeda, Jes\'{u}s and Torres, Eduard},
  title =	{{Building High Strength Mixed Covering Arrays with Constraints}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{12:1--12:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.12},
  URN =		{urn:nbn:de:0030-drops-153036},
  doi =		{10.4230/LIPIcs.CP.2021.12},
  annote =	{Keywords: Combinatorial Testing, Covering Arrays, Maximum Satisfiability}
}

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