26 Search Results for "Lynce, Inês"

Document
DOI: 10.4230/DagRep.15.8.80
Optimization and Automated Reasoning for Designing Future Space Missions (Dagstuhl Seminar 25362)

Authors: Max Bannach, Johannes Klaus Fichte, Dario Izzo, Inês Lynce, and Giacomo Acciarini

Published in: Dagstuhl Reports, Volume 15, Issue 8 (2026)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25362 Optimization and Automated Reasoning for Designing Future Space Missions, which explored fundamental optimization and reasoning tasks that arise in early stages of designing complex space missions. Such tasks include selecting and scheduling the bodies that should be encountered, routing a spacecraft across multiple bodies optimally, or strategically placing facilities to support future missions. Many of these problems are still solved by hand, as current missions only contain a few celestial objects. However, with larger and increasingly complex missions, these problems become more relevant and, thus, there is an increasing need to solve space-related optimization, scheduling, and planning problems automatically. Despite the promising opportunities for collaboration, the entry barrier to many of these problems remains high for those without a background in celestial mechanics. Conversely, modern tools and techniques from constraint reasoning and optimization are still largely unfamiliar to many aerospace researchers. The Dagstuhl Seminar 25362 successfully established a bridge between computer scientists working in automated reasoning and experts from the space domain focused on mission analysis and operations. This Dagstuhl Seminar brought together researchers from academia, industry, and space agencies, fostering interdisciplinary dialogue. Problems and tools from both communities were presented in a language accessible to the other, laying the groundwork for future joint research and development.
Cite as

Max Bannach, Johannes Klaus Fichte, Dario Izzo, Inês Lynce, and Giacomo Acciarini. Optimization and Automated Reasoning for Designing Future Space Missions (Dagstuhl Seminar 25362). In Dagstuhl Reports, Volume 15, Issue 8, pp. 80-94, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{bannach_et_al:DagRep.15.8.80,
  author =	{Bannach, Max and Fichte, Johannes Klaus and Izzo, Dario and Lynce, In\^{e}s and Acciarini, Giacomo},
  title =	{{Optimization and Automated Reasoning for Designing Future Space Missions (Dagstuhl Seminar 25362)}},
  pages =	{80--94},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2026},
  volume =	{15},
  number =	{8},
  editor =	{Bannach, Max and Fichte, Johannes Klaus and Izzo, Dario and Lynce, In\^{e}s and Acciarini, Giacomo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.8.80},
  URN =		{urn:nbn:de:0030-drops-257771},
  doi =		{10.4230/DagRep.15.8.80},
  annote =	{Keywords: Automated Reasoning, Satellite Constellation Design, Space Logistics, Trajectory Optimization, Astrodynamics, Global Trajectory Optimization}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2025.25
On the Interplay of Cube Learning and Dependency Schemes in {QCDCL} Proof Systems

Authors: Abhimanyu Choudhury and Meena Mahajan

Published in: LIPIcs, Volume 360, 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025)

Abstract
Quantified Conflict Driven Clause Leaning (QCDCL) is one of the main approaches to solving Quantified Boolean Formulas (QBF). Cube-learning is employed in this approach to ensure that true formulas can be verified. Dependency Schemes help to detect spurious dependencies that are implied by the variable ordering in the quantifier prefix of QBFs but are not essential for constructing (counter)models. This detection can provably shorten refutations in specific proof systems, and is expected to speed up runs of QBF solvers. The simplest underlying proof system [BeyersdorffBöhm-LMCS2023], formalises the reasoning in the QCDCL approach on false formulas, when neither cube-learning nor dependency schemes is used. The work of [BöhmPeitlBeyersdorff-AI2024] further incorporates cube-learning. The work of [ChoudhuryMahajan-JAR2024] incorporates a limited use of dependency schemes, but without cube-learning. In this work, proof systems underlying the reasoning of QCDCL solvers which use cube learning, and which use dependency schemes at all stages, are formalised. Sufficient conditions for soundness and completeness are presented, and it is shown that using the standard and reflexive resolution path dependency schemes (𝙳^{std} and 𝙳^{rrs}) to relax the decision order provably shortens refutations. When the decisions are restricted to follow quantification order, but dependency schemes are used in propagation and learning, in conjunction with cube-learning, the resulting proof systems using the dependency schemes 𝙳^{std} and 𝙳^{rrs} are investigated in detail and their relative strengths are analysed.
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Abhimanyu Choudhury and Meena Mahajan. On the Interplay of Cube Learning and Dependency Schemes in {QCDCL} Proof Systems. In 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 360, pp. 25:1-25:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{choudhury_et_al:LIPIcs.FSTTCS.2025.25,
  author =	{Choudhury, Abhimanyu and Mahajan, Meena},
  title =	{{On the Interplay of Cube Learning and Dependency Schemes in \{QCDCL\} Proof Systems}},
  booktitle =	{45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025)},
  pages =	{25:1--25:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-406-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{360},
  editor =	{Aiswarya, C. and Mehta, Ruta and Roy, Subhajit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2025.25},
  URN =		{urn:nbn:de:0030-drops-251062},
  doi =		{10.4230/LIPIcs.FSTTCS.2025.25},
  annote =	{Keywords: QBF, CDCL, Resolution, Dependency schemes}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.RW.2024/2025.8
ASP Essentials: Modelling and Efficient Solving (Invited Paper)

Authors: Giuseppe Mazzotta and Francesco Ricca

Published in: OASIcs, Volume 138, Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025)

Abstract
Answer Set Programming (ASP) is a logic-based Knowledge Representation and Reasoning (KRR) paradigm that facilitates rapid prototyping of solutions for complex problems. It is particularly effective for tackling Deep Reasoning tasks involving exponentially large search spaces, such as combinatorial search and optimization. While getting started with ASP is relatively easy, mastering its advanced constructs and scaling solutions to real-world problem sizes can be challenging. This paper provides an introduction to ASP, guiding the reader from the fundamentals of the language to the application of programming methodologies and the computation of answer sets. Beyond the core framework, the paper also examines selected extensions of ASP that enable the modeling of complex problems, as well as compilation techniques designed to enhance solving efficiency. Furthermore, it mentions some recent tools that combine ASP with LLMs.
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Giuseppe Mazzotta and Francesco Ricca. ASP Essentials: Modelling and Efficient Solving (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mazzotta_et_al:OASIcs.RW.2024/2025.8,
  author =	{Mazzotta, Giuseppe and Ricca, Francesco},
  title =	{{ASP Essentials: Modelling and Efficient Solving}},
  booktitle =	{Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 \& RW 2025)},
  pages =	{8:1--8:21},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-405-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{138},
  editor =	{Artale, Alessandro and Bienvenu, Meghyn and Garc{\'\i}a, Yazm{\'\i}n Ib\'{a}\~{n}ez and Murlak, Filip},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.RW.2024/2025.8},
  URN =		{urn:nbn:de:0030-drops-250539},
  doi =		{10.4230/OASIcs.RW.2024/2025.8},
  annote =	{Keywords: Answer Set Programming, ASP with Quantifiers, Grounding Bottleneck, Compilation-based ASP solving, Neurosymbolic AI, LLMs}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.35
Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems

Authors: Konstantin Sidorov, Imko Marijnissen, and Emir Demirović

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Constraint programming solvers have seen much success in scheduling problems owing to their efficient reasoning over constraints to solve complex problems in practice. Many algorithms have been proposed for propagating information from a single constraint. However, inferring and exchanging information across multiple constraints can provide deeper insight into the global structure of a problem. In this work, we propose to exchange information amongst constraints by inferring the disjointness of tasks in scheduling problems from many constraints. We do this by (i) augmenting existing propagators, such as the Cumulative and nogoods, to report when pairs of tasks are disjoint, and (ii) leveraging this information by introducing the SelectiveDisjunctive propagator which generates a lower bound on the earliest completion time of cliques of disjoint tasks to determine conflicts. This allows us to aggregate disjointness information spanning multiple constraints to gain a better global overview of the problem, as well as more precise local information. We also identify a problem structure where an LCG solver reasoning over Cumulative constraints separately, without any reformulations, requires an exponential amount of time to prove infeasibility, which we both justify theoretically and show empirically; on the other hand, our approach solves those instances in polynomial time. On particular known RCPSP and RCPSP/max benchmarks, our approach significantly reduces the number of conflicts required to prove optimality when resource contention is high. Additionally, we discover new lower bounds for 16 RCPSP/max instances (closing six of them) and four RCPSP instances (closing one), as well as new upper bounds for two RCPSP/max instances and four RCPSP instances. Furthermore, we empirically analyse our proposed approach to determine which features are beneficial for performance, showing that finding cliques is one of the main bottlenecks and that detecting disjointness during search can lead to improved bounds on certain instances, but it generally negatively impacts learning. This work paves the way for reasoning over the disjointness of tasks inferred from a variety of standard constraints to discover novel information sourced from multiple constraints during search.
Cite as

Konstantin Sidorov, Imko Marijnissen, and Emir Demirović. Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 35:1-35:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sidorov_et_al:LIPIcs.CP.2025.35,
  author =	{Sidorov, Konstantin and Marijnissen, Imko and Demirovi\'{c}, Emir},
  title =	{{Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{35:1--35:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.35},
  URN =		{urn:nbn:de:0030-drops-238969},
  doi =		{10.4230/LIPIcs.CP.2025.35},
  annote =	{Keywords: Constraint Programming, Lazy Clause Generation, Propagation, Scheduling, Cumulative, Disjunctive}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.34
DynamicSAT: Dynamic Configuration Tuning for SAT Solving

Authors: Zhengyuan Shi, Wentao Jiang, Xindi Zhang, Jin Luo, Yun Liang, Zhufei Chu, and Qiang Xu

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Boolean Satisfiability (SAT) problem serves as a foundation for solving numerous real-world challenges. As problem complexity increases, so does the demand for sophisticated SAT solvers, which incorporate a variety of heuristics tailored to optimize performance for specific problem instances. However, a major limitation persists: a configuration that performs well on one instance may lead to inefficiencies on others. While previous approaches to automatic algorithm configuration set parameters prior to runtime, they fail to adapt to the dynamic evolution of problem characteristics during the solving process. We introduce DynamicSAT, a novel SAT solver framework that dynamically tunes configuration parameters during solving process. By adjusting parameters on-the-fly, DynamicSAT adapts to changes arising from clause learning, elimination, and other transformations, thus improving efficiency and robustness across diverse SAT instances. We demonstrate that DynamicSAT achieves significant performance gains over the state-of-the-art solver on 2024 SAT Competition Benchmark.
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Zhengyuan Shi, Wentao Jiang, Xindi Zhang, Jin Luo, Yun Liang, Zhufei Chu, and Qiang Xu. DynamicSAT: Dynamic Configuration Tuning for SAT Solving. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 34:1-34:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shi_et_al:LIPIcs.CP.2025.34,
  author =	{Shi, Zhengyuan and Jiang, Wentao and Zhang, Xindi and Luo, Jin and Liang, Yun and Chu, Zhufei and Xu, Qiang},
  title =	{{DynamicSAT: Dynamic Configuration Tuning for SAT Solving}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{34:1--34:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.34},
  URN =		{urn:nbn:de:0030-drops-238952},
  doi =		{10.4230/LIPIcs.CP.2025.34},
  annote =	{Keywords: Boolean satisfiability problem, configuration tuning, multi-armed bandit}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.36
Balancing Latin Rectangles with LLM-Generated Streamliners

Authors: Florentina Voboril, Vaidyanathan Peruvemba Ramaswamy, and Stefan Szeider

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We present an integration of Large Language Models (LLMs) with streamlining techniques to find well-balanced Latin rectangles. Our approach combines LLM-generated streamlining constraints that effectively partition the search space, directing constraint solvers toward structured subspaces containing high-quality solutions. Our methodology extends LLM-generated streamliners, as Voboril et al. (2024) introduced for decision problems, to the optimization context through techniques that incrementally refine the objective function value. We propose two complementary strategies to orchestrate sets of streamliners: an incremental mechanism that utilizes improving solutions to initialize subsequent search processes, and an evolutionary framework that maintains and refines effective streamliner populations. Our experiments demonstrate that our approach successfully reduces established minimum imbalance values for partially spatially balanced Latin rectangles across multiple problem dimensions. The results validate the efficacy of combining LLMs with constraint programming methodologies for tackling problems characterized by complex global constraints.
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Florentina Voboril, Vaidyanathan Peruvemba Ramaswamy, and Stefan Szeider. Balancing Latin Rectangles with LLM-Generated Streamliners. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 36:1-36:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{voboril_et_al:LIPIcs.CP.2025.36,
  author =	{Voboril, Florentina and Peruvemba Ramaswamy, Vaidyanathan and Szeider, Stefan},
  title =	{{Balancing Latin Rectangles with LLM-Generated Streamliners}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{36:1--36:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.36},
  URN =		{urn:nbn:de:0030-drops-238970},
  doi =		{10.4230/LIPIcs.CP.2025.36},
  annote =	{Keywords: Balanced Latin Rectangles, Streamliners, Large Language Models, Warmstarts, Evolutionary Search}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.6
Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem

Authors: Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, and Guillaume Poveda

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We present an industrial case on workforce allocation and scheduling in the aircraft manufacturing industry, where available teams need to be assigned to logistical operations. This application presents several challenges such as the scale of the problem, the need for fair workload distribution, and the need for methods for mitigating unforeseen disruptions due to technical malfunctions or incompatible weather conditions. We compare different Constraint Programming (CP) models for the allocation and scheduling problems, with extra focus on modeling the workload balancing component. Additionally, we investigate different techniques for explaining infeasibility of a disrupted schedule, such as conflict computation using Minimal Unsatisfiable Subsets (MUSes) and feasibility restoration using Minimal Correction Subsets (MCSes) or constraint relaxations. Our experimental results show that by using appropriate modeling techniques, the problem can be solved in reasonable time, thereby producing fair schedules. Additionally, we show how invalidated schedules can be explained and restored efficiently to help human operators in solving disruptions to the schedule.
Cite as

Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, and Guillaume Poveda. Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 6:1-6:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bleukx_et_al:LIPIcs.CP.2025.6,
  author =	{Bleukx, Ignace and Boumazouza, Ryma and Guns, Tias and Laage, Nadine and Poveda, Guillaume},
  title =	{{Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{6:1--6:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.6},
  URN =		{urn:nbn:de:0030-drops-238670},
  doi =		{10.4230/LIPIcs.CP.2025.6},
  annote =	{Keywords: modeling, scheduling, fairness, explanations, feasibility restoration}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.9
Constraint Models for Klondike

Authors: Nguyen Dang, Ian P. Gent, Peter Nightingale, Felix Ulrich-Oltean, and Jack Waller

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Klondike is the most famous single-player card game, and remains a challenging search problem even in the "thoughtful" variant where all card locations are known. We consider the full game of Klondike except for one restriction that the unusual move of "worrying back" is disallowed. This model is able to determine the winnability of all instances of the game and in practice does so in less than 2000 secs for 10,000 instances we tested, which no other known algorithm can achieve. On some instances, however, other techniques can produce answers more quickly. We use constraint modelling to produce schedules for running our constraint model in combination with other techniques. The combination outperforms any single solver across a range of time limits. Using this combination we are able to significantly improve the best estimate of winnability of Klondike without worrying back. Finally we show how we can use this work to also improve the estimate of winnability of the regular game of Klondike.
Cite as

Nguyen Dang, Ian P. Gent, Peter Nightingale, Felix Ulrich-Oltean, and Jack Waller. Constraint Models for Klondike. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dang_et_al:LIPIcs.CP.2025.9,
  author =	{Dang, Nguyen and Gent, Ian P. and Nightingale, Peter and Ulrich-Oltean, Felix and Waller, Jack},
  title =	{{Constraint Models for Klondike}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{9:1--9:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.9},
  URN =		{urn:nbn:de:0030-drops-238702},
  doi =		{10.4230/LIPIcs.CP.2025.9},
  annote =	{Keywords: AI Planning, Modelling, Constraint Programming, Solitaire and Patience Games}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.15
Symmetric Core Learning for Pseudo-Boolean Optimization by Implicit Hitting Sets

Authors: Hannes Ihalainen, Jeremias Berg, Matti Järvisalo, and Bart Bogaerts

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We propose symmetric core learning (SCL) as a novel approach to making the implicit hitting set approach (IHS) to constraint optimization more symmetry-aware. SCL has the potential of significantly reducing the number of iterations and, in particular, the number of calls to an NP decision solver for extracting individual unsatisfiable cores. As the technique is focused on generating symmetric cores to the hitting set component of IHS, SCL is generally applicable in IHS-style search for essentially any constraint optimization paradigm. In this work, we focus in particular on integrating SCL to IHS for pseudo-Boolean optimization (PBO), as earlier proposed static symmetry breaking through lex-leader constraints generated before search turns out to often degrade the performance of the IHS approach to PBO. In contrast, we show that SCL can improve the runtime performance of a state-of-the-art IHS approach to PBO and generally does not impose significant overhead in terms of runtime performance.
Cite as

Hannes Ihalainen, Jeremias Berg, Matti Järvisalo, and Bart Bogaerts. Symmetric Core Learning for Pseudo-Boolean Optimization by Implicit Hitting Sets. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 15:1-15:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ihalainen_et_al:LIPIcs.CP.2025.15,
  author =	{Ihalainen, Hannes and Berg, Jeremias and J\"{a}rvisalo, Matti and Bogaerts, Bart},
  title =	{{Symmetric Core Learning for Pseudo-Boolean Optimization by Implicit Hitting Sets}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{15:1--15:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.15},
  URN =		{urn:nbn:de:0030-drops-238767},
  doi =		{10.4230/LIPIcs.CP.2025.15},
  annote =	{Keywords: Implicit hitting sets, symmetries, unsatisfiable cores, pseudo-Boolean optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.21
Practically Feasible Proof Logging for Pseudo-Boolean Optimization

Authors: Wietze Koops, Daniel Le Berre, Magnus O. Myreen, Jakob Nordström, Andy Oertel, Yong Kiam Tan, and Marc Vinyals

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Certifying solvers have long been standard for decision problems in Boolean satisfiability (SAT), allowing for proof logging and checking with very limited overhead, but developing similar tools for combinatorial optimization has remained a challenge. A recent promising approach covering a wide range of solving paradigms is pseudo-Boolean proof logging, but this has mostly consisted of proof-of-concept works far from delivering the performance required for real-world deployment. In this work, we present an efficient toolchain based on VeriPB and CakePB for formally verified pseudo-Boolean optimization. We implement proof logging for the full range of techniques in the state-of-the-art solvers RoundingSat and Sat4j, including core-guided search and linear programming integration with Farkas certificates and cut generation. Our experimental evaluation shows that proof logging and checking performance in this much more expressive paradigm is now quite close to the level of SAT solving, and hence is clearly practically feasible.
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Wietze Koops, Daniel Le Berre, Magnus O. Myreen, Jakob Nordström, Andy Oertel, Yong Kiam Tan, and Marc Vinyals. Practically Feasible Proof Logging for Pseudo-Boolean Optimization. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 21:1-21:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koops_et_al:LIPIcs.CP.2025.21,
  author =	{Koops, Wietze and Le Berre, Daniel and Myreen, Magnus O. and Nordstr\"{o}m, Jakob and Oertel, Andy and Tan, Yong Kiam and Vinyals, Marc},
  title =	{{Practically Feasible Proof Logging for Pseudo-Boolean Optimization}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{21:1--21:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.21},
  URN =		{urn:nbn:de:0030-drops-238825},
  doi =		{10.4230/LIPIcs.CP.2025.21},
  annote =	{Keywords: proof logging, certifying algorithms, combinatorial optimization, certification, pseudo-Boolean solving, 0-1 integer linear programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.28
SLS-Enhanced Core-Boosted Linear Search for Anytime Maximum Satisfiability

Authors: Ole Lübke and Jeremias Berg

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Maximum Satisfiability (MaxSAT), the constraint paradigm of minimizing a linear expression over Boolean (0-1) variables subject to a set of propositional clauses, is today used for solving NP-hard combinatorial optimization problems in various domains. Especially anytime MaxSAT solvers that compute low-cost solutions within a limited available computational time have significantly improved in recent years. Such solvers can be divided into SAT-based methods that use sophisticated reasoning, and stochastic local search (SLS) methods that heuristically explore the search space. The two are complementary; roughly speaking, SLS struggles with finding feasible solutions, and SAT-based methods with minimizing cost. Consequently, most state-of-the-art anytime MaxSAT solvers run SLS before a SAT-based algorithm with minimal communication between the two. In this paper, we aim to harness the complementary strengths of SAT-based, and SLS approaches in the context of anytime MaxSAT. More precisely, we describe several ways to enhance the performance of the so-called core-boosted linear search algorithm for anytime MaxSAT with SLS techniques. Core-boosted linear search is a three-phase algorithm where each phase uses different types of reasoning. Beyond MaxSAT, core-boosted search has also been successful in the related paradigms of pseudo-boolean optimization and constraint programming. We describe how an SLS approach to MaxSAT can be tightly integrated with all three phases of the algorithm, resulting in non-trivial information exchange in both directions between the SLS algorithm and the reasoning methods. We evaluate our techniques on standard benchmarks from the latest MaxSAT Evaluation and demonstrate that our techniques can noticeably improve on implementations of core-boosted search and SLS.
Cite as

Ole Lübke and Jeremias Berg. SLS-Enhanced Core-Boosted Linear Search for Anytime Maximum Satisfiability. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 28:1-28:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lubke_et_al:LIPIcs.CP.2025.28,
  author =	{L\"{u}bke, Ole and Berg, Jeremias},
  title =	{{SLS-Enhanced Core-Boosted Linear Search for Anytime Maximum Satisfiability}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{28:1--28:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.28},
  URN =		{urn:nbn:de:0030-drops-238897},
  doi =		{10.4230/LIPIcs.CP.2025.28},
  annote =	{Keywords: Maximum Satisfiability, MaxSAT, SAT, SLS, Anytime Optimization}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2025.42
Towards Modern and Modular SAT for LCG (Short Paper)

Authors: Jip J. Dekker, Alexey Ignatiev, Peter J. Stuckey, and Allen Z. Zhong

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Lazy Clause Generation (LCG) is an architecture for building Constraint Programming (CP) solvers using an underlying Boolean Satisfiability (SAT) engine. The CP propagation engine lazily creates clauses that define the integer variables and impose problem restrictions. The SAT engine uses the clausal model to reason and search, including, crucially, the generation of nogoods. However, while SAT solving has made significant advances recently, the underlying SAT technology in most LCG solvers has largely remained the same. Using a new interface to SAT engines, IPASIR-UP, we can construct an LCG solver which can swap out the underlying SAT engine with any that supports the interface. This new approach means we need to revisit many of the design and engineering decisions for LCG solvers, to take maximum advantage of a better underlying SAT engine while adhering to the restrictions of the interface. In this paper, we explore the possibilities and challenges of using IPASIR-UP for LCG, showing that it can be used to create a highly competitive solver.
Cite as

Jip J. Dekker, Alexey Ignatiev, Peter J. Stuckey, and Allen Z. Zhong. Towards Modern and Modular SAT for LCG (Short Paper). In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 42:1-42:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dekker_et_al:LIPIcs.CP.2025.42,
  author =	{Dekker, Jip J. and Ignatiev, Alexey and Stuckey, Peter J. and Zhong, Allen Z.},
  title =	{{Towards Modern and Modular SAT for LCG}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{42:1--42:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.42},
  URN =		{urn:nbn:de:0030-drops-239038},
  doi =		{10.4230/LIPIcs.CP.2025.42},
  annote =	{Keywords: Lazy Clause Generation, Boolean Satisfiability, IPASIR-UP}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.39
The 3-Decomposition Conjecture: A SAT-Based Approach with Specialized Propagators

Authors: Tianwei Zhang and Stefan Szeider

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We investigate the 3-decomposition conjecture, which states that every connected cubic graph can be decomposed into a spanning tree, a collection of cycles, and a matching. Using a SAT-based approach enhanced with specialized propagators, we verify the conjecture for all relevant graphs up to 28 vertices. Our method extends the Satisfiability Modulo Symmetries (SMS) framework with specialized propagators that exploit theoretical properties of minimal counterexamples (counterexamples with the minimal number of vertices), enabling efficient pruning. We demonstrate that graphs containing certain substructures cannot be minimal counterexamples to the conjecture, allowing us to exclude these patterns during the search dynamically. Our experimental results quantify the impact of different propagator configurations and forbidden subgraph constraints on solving efficiency, showing significant performance improvements when leveraging these techniques. The approach scales effectively to graphs of 28 vertices. Our work illustrates how combining SAT solving with specialized constraint propagation techniques can successfully address challenging combinatorial problems in contemporary graph theory.
Cite as

Tianwei Zhang and Stefan Szeider. The 3-Decomposition Conjecture: A SAT-Based Approach with Specialized Propagators. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 39:1-39:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zhang_et_al:LIPIcs.CP.2025.39,
  author =	{Zhang, Tianwei and Szeider, Stefan},
  title =	{{The 3-Decomposition Conjecture: A SAT-Based Approach with Specialized Propagators}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{39:1--39:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.39},
  URN =		{urn:nbn:de:0030-drops-239005},
  doi =		{10.4230/LIPIcs.CP.2025.39},
  annote =	{Keywords: SAT, Symmetry Breaking, Subgraphs, Propagators, Combinatorics}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.15
RustSAT: A Library for SAT Solving in Rust

Authors: Christoph Jabs

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

Abstract
State-of-the-art Boolean satisfiability (SAT) solvers constitute a practical and competitive approach for solving various real-world problems. To encourage their widespread adoption, the relatively high barrier of entry following from the low level syntax of SAT and the expert knowledge required to achieve tight integration with SAT solvers should be further reduced. We present RustSAT, a library with the aim of making SAT solving technology readily available in the Rust programming language. RustSAT provides functionality for helping with generating (Max)SAT instances, writing them to, or reading them from files. Furthermore, RustSAT includes interfaces to various state-of-the-art SAT solvers available with a unified Rust API. Lastly, RustSAT implements several encodings for higher level constraints (at-most-one, cardinality, and pseudo-Boolean), which are also available via a C and Python API.
Cite as

Christoph Jabs. RustSAT: A Library for SAT Solving in Rust. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jabs:LIPIcs.SAT.2025.15,
  author =	{Jabs, Christoph},
  title =	{{RustSAT: A Library for SAT Solving in Rust}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{15:1--15:13},
  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.2025.15},
  URN =		{urn:nbn:de:0030-drops-237498},
  doi =		{10.4230/LIPIcs.SAT.2025.15},
  annote =	{Keywords: Rust, library, SAT solvers, constraint encodings}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.3
Problem Partitioning via Proof Prefixes

Authors: Zachary Battleman, Joseph E. Reeves, and Marijn J. H. Heule

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

Abstract
Satisfiability solvers have been instrumental in tackling hard problems, including mathematical challenges that require years of computation. A key obstacle in efficiently solving such problems lies in effectively partitioning them into many, frequently millions of subproblems. Existing automated partitioning techniques, primarily based on lookahead methods, perform well on some instances but fail to generate effective partitions for many others. This paper introduces a powerful partitioning approach that leverages prefixes of proofs derived from conflict-driven clause-learning solvers. This method enables non-experts to harness the power of massively parallel SAT solving for their problems. We also propose a semantically-driven partitioning technique tailored for problems with large cardinality constraints, which frequently arise in optimization tasks. We evaluate our methods on diverse benchmarks, including combinatorial problems and formulas from SAT and MaxSAT competitions. Our results demonstrate that these techniques outperform existing partitioning strategies in many cases, offering improved scalability and efficiency.
Cite as

Zachary Battleman, Joseph E. Reeves, and Marijn J. H. Heule. Problem Partitioning via Proof Prefixes. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{battleman_et_al:LIPIcs.SAT.2025.3,
  author =	{Battleman, Zachary and Reeves, Joseph E. and Heule, Marijn J. H.},
  title =	{{Problem Partitioning via Proof Prefixes}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{3:1--3:18},
  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.2025.3},
  URN =		{urn:nbn:de:0030-drops-237378},
  doi =		{10.4230/LIPIcs.SAT.2025.3},
  annote =	{Keywords: Satisfiability solving, parallel computing, problem partitioning}
}
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