11 Search Results for "Pulina, Luca"

Document
DOI: 10.4230/LIPIcs.ITCS.2026.8
Intersection Theorems: A Potential Approach to Proof Complexity Lower Bounds

Authors: Yaroslav Alekseev and Nikita Gaevoy

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract
Recently, Göös et al. [Göös et al., 2024] showed that Res ⋏ uSA = RevRes in the following sense: if a formula φ has refutations of size at most s and width/degree at most w in both Res and uSA, then there is a refutation for φ of size at most poly(s ⋅ 2^w) in RevRes. Their proof relies on the TFNP characterization of the aforementioned proof systems. In our work, we give a direct and simplified proof of this result, simultaneously achieving better bounds: we show that if for a formula φ there are refutations of size at most s in both Res and uSA, then there is a refutation of φ of size at most poly(s) in RevRes. This potentially allows us to "lift" size lower bounds from RevRes to Res for the formulas for which there are upper bounds in uSA. This kind of lifting was not possible before because of the exponential blow-up in size from the width. Similarly, we improve the bounds in another intersection theorem from [Göös et al., 2024] by giving a direct proof of Res ⋏ uNS = RevResT. Finally, we generalize those intersection theorems to some proof systems for which we currently do not have a TFNP characterization. For example, we show that Res(⊕) ⋏ u-wRes(⊕) = RevRes(⊕), which effectively allows us to reduce the problem of proving Pigeonhole Principle lower bounds in Res(⊕) to proving Pigeonhole Principle lower bounds in RevRes(⊕), a potentially weaker proof system.
Cite as

Yaroslav Alekseev and Nikita Gaevoy. Intersection Theorems: A Potential Approach to Proof Complexity Lower Bounds. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 8:1-8:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{alekseev_et_al:LIPIcs.ITCS.2026.8,
  author =	{Alekseev, Yaroslav and Gaevoy, Nikita},
  title =	{{Intersection Theorems: A Potential Approach to Proof Complexity Lower Bounds}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{8:1--8:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.8},
  URN =		{urn:nbn:de:0030-drops-252953},
  doi =		{10.4230/LIPIcs.ITCS.2026.8},
  annote =	{Keywords: proof complexity, intersection theorems}
}
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.
Cite as

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
DOI: 10.4230/LIPIcs.CP.2025.12
An Expansion-Based Approach for Quantified Integer Programming

Authors: Michael Hartisch and Leroy Chew

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

Abstract
Quantified Integer Programming (QIP) bridges multiple domains by extending Quantified Boolean Formulas (QBF) to incorporate general integer variables and linear constraints while also generalizing Integer Programming through variable quantification. As a special case of Quantified Constraint Satisfaction Problems (QCSP), QIP provides a versatile framework for addressing complex decision-making scenarios. Additionally, the inclusion of a linear objective function enables QIP to effectively model multistage robust discrete linear optimization problems, making it a powerful tool for tackling uncertainty in optimization. While two primary solution paradigms exist for QBF - search-based and expansion-based approaches - only search-based methods have been explored for QIP and QCSP. We introduce an expansion-based approach for QIP using Counterexample-Guided Abstraction Refinement (CEGAR), adapting techniques from QBF. We extend this methodology to tackle multistage robust discrete optimization problems with linear constraints and further embed it in an optimization framework, enhancing its applicability. Our experimental results highlight the advantages of this approach, demonstrating superior performance over existing search-based solvers for QIP in specific instances. Furthermore, the ability to model problems using linear constraints enables notable performance gains over state-of-the-art expansion-based solvers for QBF.
Cite as

Michael Hartisch and Leroy Chew. An Expansion-Based Approach for Quantified Integer Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 12:1-12:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hartisch_et_al:LIPIcs.CP.2025.12,
  author =	{Hartisch, Michael and Chew, Leroy},
  title =	{{An Expansion-Based Approach for Quantified Integer Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{12:1--12:26},
  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.2025.12},
  URN =		{urn:nbn:de:0030-drops-238736},
  doi =		{10.4230/LIPIcs.CP.2025.12},
  annote =	{Keywords: Quantified Integer Programming, Quantified Constraint Satisfaction, Robust Discrete Optimization, Expansion, CEGAR}
}
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.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.2025.11
Better Extension Variables in DQBF via Independence

Authors: Leroy Chew and Tomáš Peitl

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

Abstract
We show that extension variables in (D)QBF can be generalised by conditioning on universal assignments. The benefit of this is that the dependency sets of such conditioned extension variables can be made smaller to allow easier refutations. This simple modification instantly solves many challenges in p-simulating the QBF expansion rule, which cannot be p-simulated in proof systems that have strategy extraction [Leroy Chew and Judith Clymo, 2020]. Simulating expansion is even more crucial in DQBF, where other methods are incomplete. In this paper we provide an overview of the strength of this new independent extension rule. We find that a new version of Extended Frege called IndExtFrege + ∀red can p-simulate a multitude of difficult QBF and DQBF techniques, even techniques that are difficult to approach with eFrege + ∀red. We show five p-simulations, that IndExtFrege + ∀red p-simulates QRAT, DQBF-IR-calc, IR(𝒟^rrs)-calc, Fork-Resolution and DQRAT which together underpin most DQBF solving and preprocessing techniques. The p-simulations work despite these systems using complicated rules and our new extension rule being relatively simple. Moreover, unlike recent p-simulations by eFrege + ∀red we can simulate the proof rules line by line, which allows us to mix QBF rules more easily with other inference steps.
Cite as

Leroy Chew and Tomáš Peitl. Better Extension Variables in DQBF via Independence. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 11:1-11:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chew_et_al:LIPIcs.SAT.2025.11,
  author =	{Chew, Leroy and Peitl, Tom\'{a}\v{s}},
  title =	{{Better Extension Variables in DQBF via Independence}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{11:1--11:24},
  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.2025.11},
  URN =		{urn:nbn:de:0030-drops-237453},
  doi =		{10.4230/LIPIcs.SAT.2025.11},
  annote =	{Keywords: DQBF, QBF, Proof Systems, Dependency Schemes, RAT, Extended Frege, Skolem functions}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.25
QRP+Gen: A Framework for Checking Q-Resolution Proofs with Generalized Axioms

Authors: Mark Peyrer and Martina Seidl

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

Abstract
Q-resolution is a proof system for quantified Boolean formulas (QBFs) that forms the foundation for search-based QBF solvers with clause and cube learning. To derive stronger clauses and cubes, Q-resolution was extended with so-called generalized axioms. The derivation of such generalized axioms relies on solving oracles that could be, for example, SAT solvers or even QBF solvers. While the correctness of results obtained with classical QCDCL-based solving can be efficiently certified by an independent checker, until now, proof generation had to be turned off to benefit from generalized axioms. Consequently, the results obtained with reasoning under generalized axioms could not be certified independently. To overcome this restriction, we present QRP+Gen, a novel framework to automatically generate and check Q-resolution proofs that contain generalized axioms. To this end, we extended the Q-resolution format QRP such that all necessary information is included to verify the correctness of generalized axioms. Our extension allows to integrate certificates produced by any oracle which can produce automatically checkable proofs. Furthermore, we developed a proof checker that orchestrates the proof checking of the core Q-resolution proof and the proofs produced by the oracles. As a case study, we equipped the search-based QBF solver DepQBF with proof-producing oracles for the SAT-based techniques trivial truth and trivial falsity.
Cite as

Mark Peyrer and Martina Seidl. QRP+Gen: A Framework for Checking Q-Resolution Proofs with Generalized Axioms. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 25:1-25:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{peyrer_et_al:LIPIcs.SAT.2025.25,
  author =	{Peyrer, Mark and Seidl, Martina},
  title =	{{QRP+Gen: A Framework for Checking Q-Resolution Proofs with Generalized Axioms}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{25:1--25:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.25},
  URN =		{urn:nbn:de:0030-drops-237592},
  doi =		{10.4230/LIPIcs.SAT.2025.25},
  annote =	{Keywords: Automated Reasoning, Quantified Resolution Proof, Generalized Axioms}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.23
Symbolic Conflict Analysis in Pseudo-Boolean Optimization

Authors: Robert Nieuwenhuis, Albert Oliveras, Enric Rodríguez-Carbonell, and Rui Zhao

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

Abstract
In the the last two decades, a lot of effort has been devoted to the development of satisfiability-checking tools for a variety of SAT-related problems. However, most of these tools lack optimization capabilities. That is, instead of finding any solution, one is sometimes interested in a solution that is best according to some criterion. Pseudo-Boolean solvers can be used to deal with optimization by successively solving a series of problems that contain an additional pseudo-Boolean constraint expressing that a better solution is required. A key point for the success of this simple approach is that lemmas that are learned for one problem can be reused for subsequent ones. In this paper we go one step further and show how, by using a simple symbolic conflict analysis procedure, not only can lemmas be reused between problems but also strengthened, thus further pruning the search space traversal. In addition, we show how this technique automatically allows one to infer upper bounds in maximization problems, thus giving an estimation of how far the solver is from finding an optimal solution. Experimental results with our PB solver reveal that (i) this technique is indeed effective in practice, providing important speedups in problems where several solutions are found and (ii) on problems with very few solutions, where the impact of our technique is limited, its overhead is negligible.
Cite as

Robert Nieuwenhuis, Albert Oliveras, Enric Rodríguez-Carbonell, and Rui Zhao. Symbolic Conflict Analysis in Pseudo-Boolean Optimization. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 23:1-23:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{nieuwenhuis_et_al:LIPIcs.SAT.2025.23,
  author =	{Nieuwenhuis, Robert and Oliveras, Albert and Rodr{\'\i}guez-Carbonell, Enric and Zhao, Rui},
  title =	{{Symbolic Conflict Analysis in Pseudo-Boolean Optimization}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{23:1--23:18},
  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.2025.23},
  URN =		{urn:nbn:de:0030-drops-237579},
  doi =		{10.4230/LIPIcs.SAT.2025.23},
  annote =	{Keywords: SAT, Pseudo-Boolean Optimization, Conflict Analysis}
}
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.
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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.2025.21
Improving Reduction Techniques in Pseudo-Boolean Conflict Analysis

Authors: Orestis Lomis, Jo Devriendt, Hendrik Bierlee, and Tias Guns

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

Abstract
Recent pseudo-Boolean (PB) solvers leverage the cutting planes proof system to perform SAT-style conflict analysis during search. This process learns implied PB constraints, which can prune later parts of the search tree and is crucial to a PB solver’s performance. A key step in PB conflict analysis is the reduction of a reason constraint, which caused a variable propagation that contributed to the conflict. While necessary, reduction generally makes the reason constraint less strong. Consequently, different approaches to reduction have been proposed, broadly categorised as division- or saturation-based, with the aim of preserving the strength of the reason constraint as much as possible. This paper proposes two novel techniques in each reduction category. We theoretically show how each technique yields reason constraints which are at least as strong as those obtained from existing reduction methods. We then evaluate the empirical effectiveness of the reduction techniques on hard knapsack instances and the most recent PB'24 competition benchmarks.
Cite as

Orestis Lomis, Jo Devriendt, Hendrik Bierlee, and Tias Guns. Improving Reduction Techniques in Pseudo-Boolean Conflict Analysis. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 21:1-21:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lomis_et_al:LIPIcs.SAT.2025.21,
  author =	{Lomis, Orestis and Devriendt, Jo and Bierlee, Hendrik and Guns, Tias},
  title =	{{Improving Reduction Techniques in Pseudo-Boolean Conflict Analysis}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{21:1--21:17},
  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.2025.21},
  URN =		{urn:nbn:de:0030-drops-237551},
  doi =		{10.4230/LIPIcs.SAT.2025.21},
  annote =	{Keywords: Constraint Programming, Pseudo-Boolean Reasoning, Conflict Analysis}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2012.37
Applying Machine Learning Techniques to ASP Solving

Authors: Marco Maratea, Luca Pulina, and Francesco Ricca

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

Abstract
Having in mind the task of improving the solving methods for Answer Set Programming (ASP), there are two usual ways to reach this goal: (i) extending state-of-the-art techniques and ASP solvers, or (ii) designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of- the-art solvers, and to apply machine learning techniques for choosing automatically the "best" available solver on a per-instance basis. In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, given the features of the instances in a training set and the solvers performance on these instances, we apply a classification method to inductively learn algorithm selection strategies to be applied to a test set. We report the results of an experiment considering solvers and training and test sets of instances taken from the ones submitted to the "System Track" of the 3rd ASP competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve a higher number of instances compared with any solver that entered the 3rd ASP competition.
Cite as

Marco Maratea, Luca Pulina, and Francesco Ricca. Applying Machine Learning Techniques to ASP Solving. In Technical Communications of the 28th International Conference on Logic Programming (ICLP'12). Leibniz International Proceedings in Informatics (LIPIcs), Volume 17, pp. 37-48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{maratea_et_al:LIPIcs.ICLP.2012.37,
  author =	{Maratea, Marco and Pulina, Luca and Ricca, Francesco},
  title =	{{Applying Machine Learning Techniques to ASP Solving}},
  booktitle =	{Technical Communications of the 28th International Conference on Logic Programming (ICLP'12)},
  pages =	{37--48},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-43-9},
  ISSN =	{1868-8969},
  year =	{2012},
  volume =	{17},
  editor =	{Dovier, Agostino and Santos Costa, V{\'\i}tor},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2012.37},
  URN =		{urn:nbn:de:0030-drops-36080},
  doi =		{10.4230/LIPIcs.ICLP.2012.37},
  annote =	{Keywords: Answer Set Programming, Automated Algorithm Selection, Multi-Engine solvers}
}
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