12 Search Results for "Katsirelos, George"

Document
DOI: 10.4230/LIPIcs.CP.2024.3
The Complexity of Symmetry Breaking Beyond Lex-Leader

Authors: Markus Anders, Sofia Brenner, and Gaurav Rattan

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

Abstract
Symmetry breaking is a widely popular approach to enhance solvers in constraint programming, such as those for SAT or MIP. Symmetry breaking predicates (SBPs) typically impose an order on variables and single out the lexicographic leader (lex-leader) in each orbit of assignments. Although it is NP-hard to find complete lex-leader SBPs, incomplete lex-leader SBPs are widely used in practice. In this paper, we investigate the complexity of computing complete SBPs, lex-leader or otherwise, for SAT. Our main result proves a natural barrier for efficiently computing SBPs: efficient certification of graph non-isomorphism. Our results explain the difficulty of obtaining short SBPs for important CP problems, such as matrix-models with row-column symmetries and graph generation problems. Our results hold even when SBPs are allowed to introduce additional variables. We show polynomial upper bounds for breaking certain symmetry groups, namely automorphism groups of trees and wreath products of groups with efficient SBPs.
Cite as

Markus Anders, Sofia Brenner, and Gaurav Rattan. The Complexity of Symmetry Breaking Beyond Lex-Leader. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 3:1-3:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{anders_et_al:LIPIcs.CP.2024.3,
  author =	{Anders, Markus and Brenner, Sofia and Rattan, Gaurav},
  title =	{{The Complexity of Symmetry Breaking Beyond Lex-Leader}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{3:1--3:24},
  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.2024.3},
  URN =		{urn:nbn:de:0030-drops-206881},
  doi =		{10.4230/LIPIcs.CP.2024.3},
  annote =	{Keywords: symmetry breaking, boolean satisfiability, matrix models, graph isomorphism}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.20
Constraint Modelling with LLMs Using In-Context Learning

Authors: Kostis Michailidis, Dimos Tsouros, and Tias Guns

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

Abstract
Constraint Programming (CP) allows for the modelling and solving of a wide range of combinatorial problems. However, modelling such problems using constraints over decision variables still requires significant expertise, both in conceptual thinking and syntactic use of modelling languages. In this work, we explore the potential of using pre-trained Large Language Models (LLMs) as coding assistants, to transform textual problem descriptions into concrete and executable CP specifications. We present different transformation pipelines with explicit intermediate representations, and we investigate the potential benefit of various retrieval-augmented example selection strategies for in-context learning. We evaluate our approach on 2 datasets from the literature, namely NL4Opt (optimisation) and Logic Grid Puzzles (satisfaction), and a heterogeneous set of exercises from a CP course. The results show that pre-trained LLMs have promising potential for initialising the modelling process, with retrieval-augmented in-context learning significantly enhancing their modelling capabilities.
Cite as

Kostis Michailidis, Dimos Tsouros, and Tias Guns. Constraint Modelling with LLMs Using In-Context Learning. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 20:1-20:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{michailidis_et_al:LIPIcs.CP.2024.20,
  author =	{Michailidis, Kostis and Tsouros, Dimos and Guns, Tias},
  title =	{{Constraint Modelling with LLMs Using In-Context Learning}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{20:1--20:27},
  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.2024.20},
  URN =		{urn:nbn:de:0030-drops-207053},
  doi =		{10.4230/LIPIcs.CP.2024.20},
  annote =	{Keywords: Constraint Modelling, Constraint Acquisition, Constraint Programming, Large Language Models, In-Context Learning, Natural Language Processing, Named Entity Recognition, Retrieval-Augmented Generation, Optimisation}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2024.35
On the Complexity of Integer Programming with Fixed-Coefficient Scaling (Short Paper)

Authors: Jorke M. de Vlas

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

Abstract
We give a polynomial time algorithm that solves a CSP over 𝐙 with linear inequalities of the form c^{a₁} x - c^{a₂} y ≤ b where x and y are variables, a₁, a₂ and b are parameters, and c is a fixed constant. This is a step in classifying the complexity of CSP(Γ) for first-order reducts Γ from (𝐙, < ,+,1). The algorithm works by first reducing the infinite domain to a finite domain by inferring an upper bound on the size of the smallest solution, then repeatedly merging consecutive constraints into new constraints, and finally solving the problem using arc consistency.
Cite as

Jorke M. de Vlas. On the Complexity of Integer Programming with Fixed-Coefficient Scaling (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 35:1-35:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{devlas:LIPIcs.CP.2024.35,
  author =	{de Vlas, Jorke M.},
  title =	{{On the Complexity of Integer Programming with Fixed-Coefficient Scaling}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{35:1--35:9},
  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.2024.35},
  URN =		{urn:nbn:de:0030-drops-207203},
  doi =		{10.4230/LIPIcs.CP.2024.35},
  annote =	{Keywords: constraint satisfaction problems, integer programming, CSP dichotomy}
}
Document
DOI: 10.4230/LIPIcs.SAT.2024.4
Satsuma: Structure-Based Symmetry Breaking in SAT

Authors: Markus Anders, Sofia Brenner, and Gaurav Rattan

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

Abstract
Symmetry reduction is crucial for solving many interesting SAT instances in practice. Numerous approaches have been proposed, which try to strike a balance between symmetry reduction and computational overhead. Arguably the most readily applicable method is the computation of static symmetry breaking constraints: a constraint restricting the search-space to non-symmetrical solutions is added to a given SAT instance. A distinct advantage of static symmetry breaking is that the SAT solver itself is not modified. A disadvantage is that the strength of symmetry reduction is usually limited. In order to boost symmetry reduction, the state-of-the-art tool BreakID [Devriendt et. al] pioneered the identification and tailored breaking of a particular substructure of symmetries, the so-called row interchangeability groups. In this paper, we propose a new symmetry breaking tool called satsuma. The core principle of our tool is to exploit more diverse but frequently occurring symmetry structures. This is enabled by new practical detection algorithms for row interchangeability, row-column symmetry, Johnson symmetry, and various combinations. Based on the resulting structural description, we then produce symmetry breaking constraints. We compare this new approach to BreakID on a range of instance families exhibiting symmetry. Our benchmarks suggest improved symmetry reduction in the presence of Johnson symmetry and comparable performance in the presence of row-column symmetry. Moreover, our implementation runs significantly faster, even though it identifies more diverse structures.
Cite as

Markus Anders, Sofia Brenner, and Gaurav Rattan. Satsuma: Structure-Based Symmetry Breaking in SAT. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 4:1-4:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{anders_et_al:LIPIcs.SAT.2024.4,
  author =	{Anders, Markus and Brenner, Sofia and Rattan, Gaurav},
  title =	{{Satsuma: Structure-Based Symmetry Breaking in SAT}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{4:1--4:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.4},
  URN =		{urn:nbn:de:0030-drops-205269},
  doi =		{10.4230/LIPIcs.SAT.2024.4},
  annote =	{Keywords: symmetry breaking, boolean satisfiability, graph isomorphism}
}
Document
DOI: 10.4230/LIPIcs.SAT.2024.8
Enhancing MaxSAT Local Search via a Unified Soft Clause Weighting Scheme

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

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

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

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

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@InProceedings{chu_et_al:LIPIcs.SAT.2024.8,
  author =	{Chu, Yi and Li, Chu-Min and Ye, Furong and Cai, Shaowei},
  title =	{{Enhancing MaxSAT Local Search via a Unified Soft Clause Weighting Scheme}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{8:1--8:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.8},
  URN =		{urn:nbn:de:0030-drops-205301},
  doi =		{10.4230/LIPIcs.SAT.2024.8},
  annote =	{Keywords: Weighted Partial MaxSAT, Local Search Method, Weighting Scheme}
}
Document
DOI: 10.4230/LIPIcs.SAT.2024.12
SAT Encoding of Partial Ordering Models for Graph Coloring Problems

Authors: Daniel Faber, Adalat Jabrayilov, and Petra Mutzel

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

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

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

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@InProceedings{faber_et_al:LIPIcs.SAT.2024.12,
  author =	{Faber, Daniel and Jabrayilov, Adalat and Mutzel, Petra},
  title =	{{SAT Encoding of Partial Ordering Models for Graph Coloring Problems}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{12:1--12:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.12},
  URN =		{urn:nbn:de:0030-drops-205340},
  doi =		{10.4230/LIPIcs.SAT.2024.12},
  annote =	{Keywords: Graph coloring, bandwidth coloring, SAT encodings, ILP formulations}
}
Document
DOI: 10.4230/LIPIcs.SAT.2024.17
Parallel Clause Sharing Strategy Based on Graph Structure of SAT Problem

Authors: Yoichiro Iida, Tomohiro Sonobe, and Mary Inaba

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

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

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

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@InProceedings{iida_et_al:LIPIcs.SAT.2024.17,
  author =	{Iida, Yoichiro and Sonobe, Tomohiro and Inaba, Mary},
  title =	{{Parallel Clause Sharing Strategy Based on Graph Structure of SAT Problem}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{17:1--17:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.17},
  URN =		{urn:nbn:de:0030-drops-205392},
  doi =		{10.4230/LIPIcs.SAT.2024.17},
  annote =	{Keywords: SAT Solver, Structure of SAT, Parallel application, Clause Learning}
}
Document
DOI: 10.4230/LIPIcs.SAT.2024.25
Trusted Scalable SAT Solving with On-The-Fly LRAT Checking

Authors: Dominik Schreiber

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

Abstract
Recent advances have enabled powerful distributed SAT solvers to emit proofs of unsatisfiability, which renders them as trustworthy as sequential solvers. However, this mode of operation is still lacking behind conventional distributed solving in terms of scalability. We argue that the core limiting factor of such approaches is the requirement of a single, persistent artifact at the end of solving that is then checked independently (and sequentially). As an alternative, we propose a bottleneck-free setup that exploits recent advancements in producing and processing LRAT information to immediately check all solvers' reasoning on-the-fly during solving. In terms of clause sharing, our approach transfers the guarantee of a derived clause’s soundness from the sending to the receiving side via cryptographic signatures. Experiments with up to 2432 cores (32 nodes) indicate that our approach reduces the running time overhead incurred by proof checking by an order of magnitude, down to a median overhead of ≤ 42% over non trusted solving.
Cite as

Dominik Schreiber. Trusted Scalable SAT Solving with On-The-Fly LRAT Checking. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 25:1-25:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{schreiber:LIPIcs.SAT.2024.25,
  author =	{Schreiber, Dominik},
  title =	{{Trusted Scalable SAT Solving with On-The-Fly LRAT Checking}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{25:1--25:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.25},
  URN =		{urn:nbn:de:0030-drops-205477},
  doi =		{10.4230/LIPIcs.SAT.2024.25},
  annote =	{Keywords: SAT solving, distributed algorithms, proofs}
}
Document
DOI: 10.4230/LIPIcs.SAT.2024.30
Anytime Approximate Formal Feature Attribution

Authors: Jinqiang Yu, Graham Farr, Alexey Ignatiev, and Peter J. Stuckey

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

Abstract
Widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models on the one hand and a number of crucial issues pertaining to them warrant the need for explainable artificial intelligence (XAI). A key explainability question is: given this decision was made, what are the input features which contributed to the decision? Although a range of XAI approaches exist to tackle this problem, most of them have significant limitations. Heuristic XAI approaches suffer from the lack of quality guarantees, and often try to approximate Shapley values, which is not the same as explaining which features contribute to a decision. A recent alternative is so-called formal feature attribution (FFA), which defines feature importance as the fraction of formal abductive explanations (AXp’s) containing the given feature. This measures feature importance from the view of formally reasoning about the model’s behavior. Namely, given a system of constraints logically representing the ML model of interest, computing an AXp requires finding a minimal unsatisfiable subset (MUS) of the system. It is challenging to compute FFA using its definition because that involves counting over all AXp’s (equivalently, counting over MUSes), although one can approximate it. Based on these results, this paper makes several contributions. First, it gives compelling evidence that computing FFA is intractable, even if the set of contrastive formal explanations (CXp’s), which correspond to minimal correction subsets (MCSes) of the logical system, is provided, by proving that the problem is #P-hard. Second, by using the duality between MUSes and MCSes, it proposes an efficient heuristic to switch from MCS enumeration to MUS enumeration on-the-fly resulting in an adaptive explanation enumeration algorithm effectively approximating FFA in an anytime fashion. Finally, experimental results obtained on a range of widely used datasets demonstrate the effectiveness of the proposed FFA approximation approach in terms of the error of FFA approximation as well as the number of explanations computed and their diversity given a fixed time limit.
Cite as

Jinqiang Yu, Graham Farr, Alexey Ignatiev, and Peter J. Stuckey. Anytime Approximate Formal Feature Attribution. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 30:1-30:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{yu_et_al:LIPIcs.SAT.2024.30,
  author =	{Yu, Jinqiang and Farr, Graham and Ignatiev, Alexey and Stuckey, Peter J.},
  title =	{{Anytime Approximate Formal Feature Attribution}},
  booktitle =	{27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024)},
  pages =	{30:1--30:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-334-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{305},
  editor =	{Chakraborty, Supratik and Jiang, Jie-Hong Roland},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2024.30},
  URN =		{urn:nbn:de:0030-drops-205526},
  doi =		{10.4230/LIPIcs.SAT.2024.30},
  annote =	{Keywords: Explainable AI, Formal Feature Attribution, Minimal Unsatisfiable Subsets, MUS Enumeration}
}
Document
DOI: 10.4230/LIPIcs.SAT.2023.12
An Analysis of Core-Guided Maximum Satisfiability Solvers Using Linear Programming

Authors: George Katsirelos

Published in: LIPIcs, Volume 271, 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)

Abstract
Many current complete MaxSAT algorithms fall into two categories: core-guided or implicit hitting set. The two kinds of algorithms seem to have complementary strengths in practice, so that each kind of solver is better able to handle different families of instances. This suggests that a hybrid might match and outperform either, but the techniques used seem incompatible. In this paper, we focus on PMRES and OLL, two core-guided algorithms based on max resolution and soft cardinality constraints, respectively. We show that these algorithms implicitly discover cores of the original formula, as has been previously shown for PM1. Moreover, we show that in some cases, including unweighted instances, they compute the optimum hitting set of these cores at each iteration. We also give compact integer linear programs for each which encode this hitting set problem. Importantly, their continuous relaxation has an optimum that matches the bound computed by the respective algorithms. This goes some way towards resolving the incompatibility of implicit hitting set and core-guided algorithms, since solvers based on the implicit hitting set algorithm typically solve the problem by encoding it as a linear program.
Cite as

George Katsirelos. An Analysis of Core-Guided Maximum Satisfiability Solvers Using Linear Programming. In 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 271, pp. 12:1-12:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{katsirelos:LIPIcs.SAT.2023.12,
  author =	{Katsirelos, George},
  title =	{{An Analysis of Core-Guided Maximum Satisfiability Solvers Using Linear Programming}},
  booktitle =	{26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
  pages =	{12:1--12:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-286-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{271},
  editor =	{Mahajan, Meena and Slivovsky, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.12},
  URN =		{urn:nbn:de:0030-drops-184745},
  doi =		{10.4230/LIPIcs.SAT.2023.12},
  annote =	{Keywords: maximum satisfiability, core-guided solvers, minimum hitting set problem, linear programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.7
Parallel Hybrid Best-First Search

Authors: Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
While processor frequency has stagnated over the past two decades, the number of available cores in servers or clusters is still growing, offering the opportunity for significant speed-up in combinatorial optimization. Parallelization of exact methods remains a difficult challenge. We revisit the concept of parallel Branch-and-Bound in the framework of Cost Function Networks. We show how to adapt the anytime Hybrid Best-First Search algorithm in a Master-Worker protocol. The resulting parallel algorithm achieves good load-balancing without introducing new parameters to be tuned as is the case, for example, in Embarrassingly Parallel Search (EPS). It has also a small overhead due to its light communication messages. We performed an experimental evaluation on several benchmarks, comparing our parallel algorithm to its sequential version. We observed linear speed-up in some cases. Our approach compared favourably to the EPS approach and also to a state-of-the-art parallel exact integer programming solver.
Cite as

Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry. Parallel Hybrid Best-First Search. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 7:1-7:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{beldjilali_et_al:LIPIcs.CP.2022.7,
  author =	{Beldjilali, Abdelkader and Montalbano, Pierre and Allouche, David and Katsirelos, George and de Givry, Simon},
  title =	{{Parallel Hybrid Best-First Search}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{7:1--7:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.7},
  URN =		{urn:nbn:de:0030-drops-166362},
  doi =		{10.4230/LIPIcs.CP.2022.7},
  annote =	{Keywords: Combinatorial Optimization, Parallel Branch-and-Bound, CFN}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.37
Structured Set Variable Domains in Bayesian Network Structure Learning

Authors: Fulya Trösser, Simon de Givry, and George Katsirelos

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Constraint programming is a state of the art technique for learning the structure of Bayesian Networks from data (Bayesian Network Structure Learning - BNSL). However, scalability both for CP and other combinatorial optimization techniques for this problem is limited by the fact that the basic decision variables are set variables with domain sizes that may grow super polynomially with the number of random variables. Usual techniques for handling set variables in CP are not useful, as they lead to poor bounds. In this paper, we propose using decision trees as a data structure for storing sets of sets to represent set variable domains. We show that relatively simple operations are sufficient to implement all propagation and bounding algorithms, and that the use of these data structures improves scalability of a state of the art CP-based solver for BNSL.
Cite as

Fulya Trösser, Simon de Givry, and George Katsirelos. Structured Set Variable Domains in Bayesian Network Structure Learning. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 37:1-37:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{trosser_et_al:LIPIcs.CP.2022.37,
  author =	{Tr\"{o}sser, Fulya and de Givry, Simon and Katsirelos, George},
  title =	{{Structured Set Variable Domains in Bayesian Network Structure Learning}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{37:1--37:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.37},
  URN =		{urn:nbn:de:0030-drops-166661},
  doi =		{10.4230/LIPIcs.CP.2022.37},
  annote =	{Keywords: Combinatorial Optimization, Bayesian Networks, Decision Trees}
}
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