22 Search Results for "Bogaerts, Bart"

Document
Research
DOI: 10.4230/TGDK.4.1.3
Semantically Reflected Programs

Authors: Eduard Kamburjan, Vidar Norstein Klungre, Yuanwei Qu, Rudolf Schlatte, Egor V. Kostylev, Martin Giese, and Einar Broch Johnsen

Published in: TGDK, Volume 4, Issue 1 (2026). Transactions on Graph Data and Knowledge, Volume 4, Issue 1

Abstract
This paper addresses the dichotomy between the formalization of structural and the formalization of executable behavioral knowledge by means of semantically lifted programs, which explore an intuitive connection between imperative programs and knowledge graphs. While knowledge graphs and ontologies are eminently useful to represent formal knowledge about a system’s individuals and universals, programming languages are designed to describe the system’s evolution. To address this dichotomy, we introduce a semantic lifting of the program states of an executing progam into a knowledge graph, for an object-oriented programming language. The resulting graph is exposed as a semantic reflection layer within the programming language, allowing programmers to leverage knowledge of the application domain in their programs during execution. In this paper, we formalize semantic lifting and semantic reflection for a small imperative programming language, SMOL, explain the operational aspects of the language, and consider type correctness and virtualization for runtime program queries through the semantic reflection layer. We illustrate semantic lifting and semantic reflection through a case study of geological modeling and discuss different applications of the technique. The language implementation is open source and available online.
Cite as

Eduard Kamburjan, Vidar Norstein Klungre, Yuanwei Qu, Rudolf Schlatte, Egor V. Kostylev, Martin Giese, and Einar Broch Johnsen. Semantically Reflected Programs. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 1, pp. 3:1-3:52, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{kamburjan_et_al:TGDK.4.1.3,
  author =	{Kamburjan, Eduard and Klungre, Vidar Norstein and Qu, Yuanwei and Schlatte, Rudolf and Kostylev, Egor V. and Giese, Martin and Johnsen, Einar Broch},
  title =	{{Semantically Reflected Programs}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:52},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.1.3},
  URN =		{urn:nbn:de:0030-drops-256884},
  doi =		{10.4230/TGDK.4.1.3},
  annote =	{Keywords: Knowledge Graphs, Ontologies, Object-Oriented Modelling, Imperative Programming Languages, Reflection, Type Safety}
}
Document
PACE Solver Description
DOI: 10.4230/LIPIcs.IPEC.2025.37
PACE Solver Description: Minimum Hitting Set Computation via Core-Guided MaxSAT Solving

Authors: André Schidler

Published in: LIPIcs, Volume 358, 20th International Symposium on Parameterized and Exact Computation (IPEC 2025)

Abstract
This paper describes our hybrid MaxSAT and mixed integer programming approach for finding minimum hitting sets as submitted to the 2025 PACE challenge. We also discuss hitting set specific challenges, lower bounds, preprocessing and design choices.
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André Schidler. PACE Solver Description: Minimum Hitting Set Computation via Core-Guided MaxSAT Solving. In 20th International Symposium on Parameterized and Exact Computation (IPEC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 358, pp. 37:1-37:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schidler:LIPIcs.IPEC.2025.37,
  author =	{Schidler, Andr\'{e}},
  title =	{{PACE Solver Description: Minimum Hitting Set Computation via Core-Guided MaxSAT Solving}},
  booktitle =	{20th International Symposium on Parameterized and Exact Computation (IPEC 2025)},
  pages =	{37:1--37:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-407-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{358},
  editor =	{Agrawal, Akanksha and van Leeuwen, Erik Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.IPEC.2025.37},
  URN =		{urn:nbn:de:0030-drops-251692},
  doi =		{10.4230/LIPIcs.IPEC.2025.37},
  annote =	{Keywords: hitting set, maxsat, core-guided}
}
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
Invited Paper
DOI: 10.4230/OASIcs.RW.2024/2025.5
Inconsistency-Tolerant Semantics Based on (Preferred) Repairs (Invited Paper)

Authors: Camille Bourgaux

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

Abstract
Real-world datasets are plagued by data quality issues which may render the data inconsistent w.r.t. a set of constraints, be they given by database integrity constraints or ontologies. A prominent way to handle such inconsistent data is to use inconsistency-tolerant semantics to obtain meaningful answers to queries. Most of these semantics are based on some notion of repairs, which represent ways of restoring the data consistency. The most basic kind of repairs is that of subset repairs, which are maximal consistent subsets of the dataset. However, in many scenarios, one can define preferred repairs based on some preference information. These lecture notes present inconsistency-tolerant semantics, focusing on the repair-based ones, then review different kinds of preferred repairs that have been considered in the literature. We present in particular the relationships between different kinds of preferred repairs and other notions related to inconsistency handling, the computational complexity of reasoning with (preferred) repairs, and some implementations.
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Camille Bourgaux. Inconsistency-Tolerant Semantics Based on (Preferred) Repairs (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. 5:1-5:67, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bourgaux:OASIcs.RW.2024/2025.5,
  author =	{Bourgaux, Camille},
  title =	{{Inconsistency-Tolerant Semantics Based on (Preferred) Repairs}},
  booktitle =	{Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 \& RW 2025)},
  pages =	{5:1--5:67},
  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.2024/2025.5},
  URN =		{urn:nbn:de:0030-drops-250504},
  doi =		{10.4230/OASIcs.RW.2024/2025.5},
  annote =	{Keywords: Knowledge bases, databases, inconsistency handling, repairs, preferences}
}
Document
DOI: 10.4230/LIPIcs.GD.2025.14
OOPS: Optimized One-Planarity Solver via SAT

Authors: Sergey Pupyrev

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
We present OOPS (Optimized One-Planarity Solver), a practical heuristic for recognizing 1-planar graphs and several important subclasses. A graph is 1-planar if it can be drawn in the plane such that each edge is crossed at most once - a natural generalization of planar graphs that has received increasing attention in graph drawing and beyond-planar graph theory. Although testing planarity can be done in linear time, recognizing 1-planar graphs is NP-complete, making effective practical algorithms especially valuable. The core idea of our approach is to reduce the recognition of 1-planarity to a propositional satisfiability (SAT) instance, enabling the use of modern SAT solvers to efficiently explore the search space. Despite the inherent complexity of the problem, our method is substantially faster in practice than naïve or brute-force algorithms. In addition to demonstrating the empirical performance of our solver on synthetic and real-world instances, we show how OOPS can be used as a discovery tool in theoretical graph theory. Specifically, we employ OOPS to investigate two research problems concerning 1-planarity of specific graph families. Our implementation of the algorithm is publicly available to support further exploration in the field.
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Sergey Pupyrev. OOPS: Optimized One-Planarity Solver via SAT. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 14:1-14:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pupyrev:LIPIcs.GD.2025.14,
  author =	{Pupyrev, Sergey},
  title =	{{OOPS: Optimized One-Planarity Solver via SAT}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{14:1--14:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.14},
  URN =		{urn:nbn:de:0030-drops-250004},
  doi =		{10.4230/LIPIcs.GD.2025.14},
  annote =	{Keywords: beyond planarity, 1-planar graph, SAT, book embeddings, upward 1-planarity}
}
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.
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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.2025.15
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.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.2025.7
Redundancy Rules for MaxSAT

Authors: Ilario Bonacina, Maria Luisa Bonet, Sam Buss, and Massimo Lauria

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

Abstract
The concept of redundancy in SAT leads to more expressive and powerful proof search techniques, e.g., able to express various inprocessing techniques, and originates interesting hierarchies of proof systems [Heule et.al'20, Buss-Thapen'19]. Redundancy has also been integrated in MaxSAT [Ihalainen et.al'22, Berg et.al'23, Bonacina et.al'24]. In this paper, we define a structured hierarchy of redundancy proof systems for MaxSAT, with the goal of studying its proof complexity. We obtain MaxSAT variants of proof systems such as SPR, PR, SR, and others, previously defined for SAT. All our rules are polynomially checkable, unlike [Ihalainen et.al'22]. Moreover, they are simpler and weaker than [Berg et.al'23], and possibly amenable to lower bounds. This work also complements the approach of [Bonacina et.al'24]. Their proof systems use different rule sets for soft and hard clauses, while here we propose a system using only hard clauses and blocking variables. This is easier to integrate with current solvers and proof checkers. We discuss the strength of the systems introduced, we show some limitations of them, and we give a short cost-SR proof that any assignment for the weak pigeonhole principle PHP^m_n falsifies at least m-n clauses.
Cite as

Ilario Bonacina, Maria Luisa Bonet, Sam Buss, and Massimo Lauria. Redundancy Rules for MaxSAT. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 7:1-7:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bonacina_et_al:LIPIcs.SAT.2025.7,
  author =	{Bonacina, Ilario and Bonet, Maria Luisa and Buss, Sam and Lauria, Massimo},
  title =	{{Redundancy Rules for MaxSAT}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{7:1--7:17},
  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.2025.7},
  URN =		{urn:nbn:de:0030-drops-237411},
  doi =		{10.4230/LIPIcs.SAT.2025.7},
  annote =	{Keywords: MaxSAT, Redundancy Rules, Pigeonhole Principle}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.26
Analyzing Reformulation Performance in Core-Guided MaxSAT Solving

Authors: André Schidler and Stefan Szeider

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

Abstract
Core-guided algorithms like OLL are among the best methods for solving the Maximum Satisfiability problem (MaxSAT). Although some performance characteristics of OLL have been studied, a comprehensive experimental analysis of its reformulation behavior is still missing. In this paper, we present a large-scale study on how different reformulations of a MaxSAT instance produced by OLL affect solver performance. By representing these reformulations as a directed acyclic graph (DAG), we isolate the impact of structural features - such as the size and interconnectivity of unsatisfiable cores - on solver runtime. Our extensive experimental evaluation of over 600k solver runs reveals clear correlations between DAG properties and performance outcomes. These results suggest a new avenue for designing heuristics that steer the solver toward more tractable reformulations. All OLL DAGs and performance data from our experiments are publicly available to foster further research.
Cite as

André Schidler and Stefan Szeider. Analyzing Reformulation Performance in Core-Guided MaxSAT Solving. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 26:1-26:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schidler_et_al:LIPIcs.SAT.2025.26,
  author =	{Schidler, Andr\'{e} and Szeider, Stefan},
  title =	{{Analyzing Reformulation Performance in Core-Guided MaxSAT Solving}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{26:1--26:18},
  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.2025.26},
  URN =		{urn:nbn:de:0030-drops-237605},
  doi =		{10.4230/LIPIcs.SAT.2025.26},
  annote =	{Keywords: maximum satisfiability, OLL, core-guided}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.17
Core-Guided Linear Programming-Based Maximum Satisfiability

Authors: George Katsirelos

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

Abstract
The core-guided algorithm OLL is the basis of some of the most successful algorithms for MaxSAT in recent evaluations. It works by iteratively finding cores of the formula and transforming it so that it exhibits a higher lower bound. It has recently been shown to implicitly discover cores of the original formula, as well as a compact representation of its reasoning within a linear program. In this paper, we use and extend these results to design a practical MaxSAT solver. We show an explicit linear program which matches and usually exceeds the bound computed by OLL. We show that OLL can be restated as an algorithm that explicitly computes a feasible dual solution of this linear program. This restated algorithm naturally works with an arbitrary dual solution. It can in fact be used to improve any LP representation of the MaxSAT instance. This presents a large increase of the potential design space for such algorithms. We describe some potential improvements from this insight and show that an implementation outperforms the state of the art algorithms on the set of instances from the latest MaxSAT evaluation.
Cite as

George Katsirelos. Core-Guided Linear Programming-Based Maximum Satisfiability. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 17:1-17:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{katsirelos:LIPIcs.SAT.2025.17,
  author =	{Katsirelos, George},
  title =	{{Core-Guided Linear Programming-Based Maximum Satisfiability}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{17:1--17:17},
  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.2025.17},
  URN =		{urn:nbn:de:0030-drops-237513},
  doi =		{10.4230/LIPIcs.SAT.2025.17},
  annote =	{Keywords: maximum satisfiability, core-guided solvers, linear programming}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.32
Efficient Certified Reasoning for Binarized Neural Networks

Authors: Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel

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

Abstract
Neural networks have emerged as essential components in safety-critical applications - these use cases demand complex, yet trustworthy computations. Binarized Neural Networks (BNNs) are a type of neural network where each neuron is constrained to a Boolean value; they are particularly well-suited for safety-critical tasks because they retain much of the computational capacities of full-scale (floating-point or quantized) deep neural networks, but remain compatible with satisfiability solvers for qualitative verification and with model counters for quantitative reasoning. However, existing methods for BNN analysis suffer from either limited scalability or susceptibility to soundness errors, which hinders their applicability in real-world scenarios. In this work, we present a scalable and trustworthy approach for both qualitative and quantitative verification of BNNs. Our approach introduces a native representation of BNN constraints in a custom-designed solver for qualitative reasoning, and in an approximate model counter for quantitative reasoning. We further develop specialized proof generation and checking pipelines with native support for BNN constraint reasoning, ensuring trustworthiness for all of our verification results. Empirical evaluations on a BNN robustness verification benchmark suite demonstrate that our certified solving approach achieves a 9× speedup over prior certified CNF and PB-based approaches, and our certified counting approach achieves a 218× speedup over the existing CNF-based baseline. In terms of coverage, our pipeline produces fully certified results for 99% and 86% of the qualitative and quantitative reasoning queries on BNNs, respectively. This is in sharp contrast to the best existing baselines which can fully certify only 62% and 4% of the queries, respectively.
Cite as

Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel. Efficient Certified Reasoning for Binarized Neural Networks. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 32:1-32:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{yang_et_al:LIPIcs.SAT.2025.32,
  author =	{Yang, Jiong and Tan, Yong Kiam and Soos, Mate and Myreen, Magnus O. and Meel, Kuldeep S.},
  title =	{{Efficient Certified Reasoning for Binarized Neural Networks}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{32:1--32:22},
  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.2025.32},
  URN =		{urn:nbn:de:0030-drops-237665},
  doi =		{10.4230/LIPIcs.SAT.2025.32},
  annote =	{Keywords: Neural network verification, proof certification, SAT solving, approximate model counting}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.27
Streamlining Distributed SAT Solver Design

Authors: Dominik Schreiber, Niccolò Rigi-Luperti, and Armin Biere

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

Abstract
Distributed clause-sharing SAT solvers have recently been established as powerful automated reasoning tools that can conquer previously infeasible instances. A common design of distributed SAT solvers is to run many off-the-shelf sequential solvers in parallel, employ some diversification (e.g., restart intervals or decision orders), and share conflict clauses among the solver threads. This approach, naïvely, adopts all best practices of sequential solver design for distributed solving, where these practices may be less useful or even actively detrimental. In this work we diagnose such shortcomings in the state-of-the-art system MallobSat and propose first effective mitigations. In particular, we replace the redundant pre- and inprocessing at all threads with single-core preprocessing that runs next to the parallel search, remove LBD values from the clause-sharing operation, and slim down solver diversification to very few lightweight and uniform methods. Experimental evaluations on up to 3072 cores (64 nodes) confirm that our measures improve performance while also drastically simplifying the SAT solving program that is run in parallel.
Cite as

Dominik Schreiber, Niccolò Rigi-Luperti, and Armin Biere. Streamlining Distributed SAT Solver Design. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schreiber_et_al:LIPIcs.SAT.2025.27,
  author =	{Schreiber, Dominik and Rigi-Luperti, Niccol\`{o} and Biere, Armin},
  title =	{{Streamlining Distributed SAT Solver Design}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{27:1--27:23},
  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.2025.27},
  URN =		{urn:nbn:de:0030-drops-237615},
  doi =		{10.4230/LIPIcs.SAT.2025.27},
  annote =	{Keywords: Satisfiability, parallel SAT solving, distributed computing, preprocessing}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.4
Certifying Without Loss of Generality Reasoning in Solution-Improving Maximum Satisfiability

Authors: Jeremias Berg, Bart Bogaerts, Jakob Nordström, Andy Oertel, Tobias Paxian, and Dieter Vandesande

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

Abstract
Proof logging has long been the established method to certify correctness of Boolean satisfiability (SAT) solvers, but has only recently been introduced for SAT-based optimization (MaxSAT). The focus of this paper is solution-improving search (SIS), in which a SAT solver is iteratively queried for increasingly better solutions until an optimal one is found. A challenging aspect of modern SIS solvers is that they make use of complex "without loss of generality" arguments that are quite involved to understand even at a human meta-level, let alone to express in a simple, machine-verifiable proof. In this work, we develop pseudo-Boolean proof logging methods for solution-improving MaxSAT solving, and use them to produce a certifying version of the state-of-the-art solver Pacose with VeriPB proofs. Our experimental evaluation demonstrates that this approach works in practice. We hope that this is yet another step towards general adoption of proof logging in MaxSAT solving.
Cite as

Jeremias Berg, Bart Bogaerts, Jakob Nordström, Andy Oertel, Tobias Paxian, and Dieter Vandesande. Certifying Without Loss of Generality Reasoning in Solution-Improving Maximum Satisfiability. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 4:1-4:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{berg_et_al:LIPIcs.CP.2024.4,
  author =	{Berg, Jeremias and Bogaerts, Bart and Nordstr\"{o}m, Jakob and Oertel, Andy and Paxian, Tobias and Vandesande, Dieter},
  title =	{{Certifying Without Loss of Generality Reasoning in Solution-Improving Maximum Satisfiability}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{4:1--4:28},
  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.2024.4},
  URN =		{urn:nbn:de:0030-drops-206895},
  doi =		{10.4230/LIPIcs.CP.2024.4},
  annote =	{Keywords: proof logging, certifying algorithms, MaxSAT, solution-improving search, SAT-UNSAT, maximum satisfiability, combinatorial optimization, certification, pseudo-Boolean}
}
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