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Towards Industrial-Like Random SAT Instances

Carlos Ansótegui, Maria Luisa Bonet, Jordi Levy
2009 International Joint Conference on Artificial Intelligence  
We focus on the random generation of SAT instances that have computational properties that are similar to real-world instances.  ...  We show how these models will allow us to generate random instances similar to industrial instances, of interest for testing purposes.  ...  Finally, as a test of how real our random instances look like, we have compared the time required by solvers specialized in industrial instances with the time required by solvers specialized in random  ... 
dblp:conf/ijcai/AnsoteguiBL09 fatcat:ryvmlcp56zemjpntfcvla6lxky

Sharpness of the Satisfiability Threshold for Non-Uniform Random k-SAT

Tobias Friedrich, Ralf Rothenberger
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
We study a more general model to generate random instances of Propositional Satisfiability (SAT) with n Boolean variables, m clauses, and exactly k variables per clause.  ...  Therefore, we call it non-uniform random k-SAT.  ...  Such SAT formulas arising from practical and industrial problems are commonly referred to as industrial SAT instances.  ... 
doi:10.24963/ijcai.2019/853 dblp:conf/ijcai/0001R19 fatcat:x6mnkhzes5br3pc4kfq2oq6wiy

On the Empirical Time Complexity of Scale-Free 3-SAT at the Phase Transition [chapter]

Thomas Bläsius, Tobias Friedrich, Andrew M. Sutton
2019 Lecture Notes in Computer Science  
Industrial instances exhibit sharply different structure than uniform random instances.  ...  On industrial SAT instances, this phenomenon is inverted: backtracking solvers can tackle large industrial problems, where SLS-based solvers appear to stall.  ...  The first two insights are a step towards understanding the disaccording runtime behaviors of different solvers on industrial and random instances.  ... 
doi:10.1007/978-3-030-17462-0_7 fatcat:tgbcl5pbjffipgrgxvu7xhjjvy

On the Structure of Industrial SAT Instances [chapter]

Carlos Ansótegui, María Luisa Bonet, Jordi Levy
2009 Lecture Notes in Computer Science  
In this paper we study many families of industrial SAT instances used in SAT competitions, and show that most of them also present this scale-free structure.  ...  On the contrary, random SAT instances, viewed as graphs, are closer to the classical random graph model, where arity of nodes follows a Poisson distribution with small variability.  ...  We think that the present study provides a step towards a theoretical explanation of why some SAT solvers perform better on industrial instances, and others on random SAT instances.  ... 
doi:10.1007/978-3-642-04244-7_13 fatcat:vm6zfjroxbarxagy7ev4pd4cny

The Fractal Dimension of SAT Formulas [chapter]

Carlos Ansótegui, Maria Luisa Bonet, Jesús Giráldez-Cru, Jordi Levy
2014 Lecture Notes in Computer Science  
Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental testing process.  ...  We explore how the dimension of a formula, together with other graph properties can be used to characterize SAT instances.  ...  One of them, is the generation of industrial-like random SAT instances.  ... 
doi:10.1007/978-3-319-08587-6_8 fatcat:buzy72ifgjdpnf5mna3qkcyedq

Evaluating Instance Generators by Configuration [chapter]

Sam Bayless, Dave A. D. Tompkins, Holger H. Hoos
2014 Lecture Notes in Computer Science  
Since the supply of instances from real-world applications of SAT is limited, and artificial instance distributions such as Uniform Random k-SAT are known to have markedly different structure, there has  ...  been a long-standing interest in instance generators capable of producing 'realistic' SAT instances that could be used during development as proxies for real-world instances.  ...  We note that this premise provides the core motivation for developing random generators for 'industrial-like' SAT instances.  ... 
doi:10.1007/978-3-319-09584-4_6 fatcat:6jzgo5hdqvccnartmscdgdnxti

Community Structure in Industrial SAT Instances [article]

Carlos Ansótegui, Maria Luisa Bonet, Jesús Giráldez-Cru, Jordi Levy, Laurent Simon
2019 arXiv   pre-print
On the contrary, random SAT instances are closer to the classical Erd\"os-R\'enyi random graph model, where no structure can be observed.  ...  In this paper, inspired by the results on complex networks, we study the community structure, or modularity, of industrial SAT instances.  ...  We think that the present study provides a step towards a theoretical explanation of why some SAT solvers perform better on industrial instances, and others on random SAT instances.  ... 
arXiv:1606.03329v3 fatcat:5a54jkhrtrhnrgud4ynqiieszq

Tailoring Local Search for Partial MaxSAT

Shaowei Cai, Chuan Luo, John Thornton, Kaile Su
2014 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT.  ...  For the industrial benchmark, {\it Dist} dramatically outperforms previous local search algorithms and is comparable with complete algorithms.  ...  For this industrial benchmark, the two SAT-based complete solvers QMaxSAT2-mt and optmax-it are the best two solvers for these industrial instances.  ... 
doi:10.1609/aaai.v28i1.9109 fatcat:zlgvhieqbvh43dttueccbqo6we

On the Temperature of SAT Formulas [chapter]

Jesús Giráldez-Cru, Pedro Almagro-Blanco
2021 Frontiers in Artificial Intelligence and Applications  
Our solution is a first step towards a hardness oracle based on the temperature of SAT formulas, which may be able to estimate the cost of solving real-world SAT instances without solving them.  ...  Interestingly, these industrial SAT problems are commonly believed to be easier than classical random SAT formulas, but estimating their actual hardness is still a very challenging question, which in some  ...  Because of this heterogeneity, realistic pseudo-industrial random SAT instances generators have emerged, stated as one of the most important challenges in propositional search [32] .  ... 
doi:10.3233/faia210115 fatcat:ekkeegannfe3hcvw5jj2z5c54y

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers [article]

Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
2019 arXiv   pre-print
Our framework is based on the idea of propagation, decimation and prediction (and hence the name PDP) in graphical models, and can be trained directly toward solving CSP in a fully unsupervised manner  ...  Our experimental results demonstrate the effectiveness of our framework for SAT solving compared to both neural and the state-of-the-art baselines.  ...  On the other hand, synthetically generating random industrial SAT instances is an active area of research in the SAT community.  ... 
arXiv:1903.01969v1 fatcat:drgloat33rdffk3vwmpj6nv3fy

Community Structure in Industrial SAT Instances

Carlos Ansótegui, Maria Luisa Bonet, Jesús Giráldez-Cru, Jordi Levy, Laurent Simon
2019 The Journal of Artificial Intelligence Research  
On the contrary, random SAT instances are closer to the classical Erdös-Rényi random graph model, where no structure can be observed.  ...  Modern SAT solvers have experienced a remarkable progress on solving industrial instances.  ...  We think that the present study provides a step towards an explanation of why some SAT solvers perform better on industrial instances, and others on random SAT formulas.  ... 
doi:10.1613/jair.1.11741 fatcat:rxfk36nfczegrprhnqcyxfxlmq

Eigenvector Centrality in Industrial SAT Instances [chapter]

George Katsirelos, Laurent Simon
2012 Lecture Notes in Computer Science  
Despite the success of modern SAT solvers on industrial instances, most of the progress relies on intensive experimental testing of improvements or new ideas.  ...  More precisely, we identify an essential structural property of industrial instances, based on the Eigenvector centrality of a graphical representation of the formula.  ...  It is understood that real world instances are different from uniform random instances, but only recently has some progress been made toward characterizing this structure by finding that these instances  ... 
doi:10.1007/978-3-642-33558-7_27 fatcat:l5ny5gmz7jcxpkkg3mo5w4ltqe

Beyond the structure of SAT formulas

Jesús Giráldez-Cru
2016 Constraints  
Towards industrial-like ran- dom SAT instances.  ...  Creating industrial-like SAT instances by clustering and reconstruction.  ... 
doi:10.1007/s10601-016-9260-z fatcat:dj2feerovvfwxdk5toynlohcga

Experiments with Massively Parallel Constraint Solving

Lucas Bordeaux, Youssef Hamadi, Horst Samulowitz
2009 International Joint Conference on Artificial Intelligence  
The computing industry is currently facing a major architectural shift.  ...  when using partially fixed variable orderings Figure 4 : 4 Figure 4: Partially Fixed Variable Ordering on 100 SAT industrial instances Figure 5 : 5 Figure 5: Detailed analysis: Satelite (l); random  ...  Since the variable ordering in SAT is extremely dynamic, SAT solvers do not exactly prove unsatisfiability by exhausting a search tree (clause learning is also used), and industrial SAT instances normally  ... 
dblp:conf/ijcai/BordeauxHS09 fatcat:q7hzwwbxszel5fj6jgq3tjfz6a

The Configurable SAT Solver Challenge (CSSC)

Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger Hoos, Kevin Leyton-Brown
2017 Artificial Intelligence  
As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances.  ...  In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track  ...  It featured three tracks mirroring those of the SAT competition: Industrial SAT+UNSAT , crafted SAT+UNSAT , and Random SAT+UNSAT .  ... 
doi:10.1016/j.artint.2016.09.006 fatcat:vf47b3ms3nestoftcseobatzv4
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