# A Model of Random Industrial SAT

@article{BarakPelleg2019AMO, title={A Model of Random Industrial SAT}, author={Dina Barak-Pelleg and Daniel Berend and J. C. Saunders}, journal={ArXiv}, year={2019}, volume={abs/1908.00089} }

One of the most studied models of SAT is random SAT. In this model, instances are composed from clauses chosen uniformly randomly and independently of each other. This model may be unsatisfactory in that it fails to describe various features of SAT instances, arising in real-world applications. Various modifications have been suggested to define models of industrial SAT. Here, we focus on community-structured SAT. Namely, the set of variables consists of a number of disjoint communities, and…

## References

SHOWING 1-10 OF 47 REFERENCES

Towards Industrial-Like Random SAT Instances

- Mathematics, Computer ScienceIJCAI
- 2009

This work provides different generation models of SAT instances, extending the uniform and regular 3-CNF models, based on the use of non-uniform probability distributions to select variables that will allow us to generate random instances similar to industrial instances, of interest for testing purposes.

Generating SAT instances with community structure

- Mathematics, Computer ScienceArtif. Intell.
- 2016

This work uses the notion of modularity to define a new model of generation of random SAT instances with community structure, called Community Attachment, and proves that the phase transition point, if exists, is independent on the modularity.

A Modularity-Based Random SAT Instances Generator

- Computer ScienceIJCAI
- 2015

It is proved that the phase transition point, if exists, is independent on the modularity and evaluated the adequacy of this model to real industrial problems in terms of SAT solvers performance, and shows that modern solvers do actually exploit this community structure.

On the Structure of Industrial SAT Instances

- Mathematics, Computer ScienceCP
- 2009

This paper study many families of industrial SAT instances used in SAT competitions, and show that most of them also present this scale-free structure, and study how the structure is modified during the execution of a SAT solver, concluding that the scale- free structure is preserved.

Approximating the unsatisfiability threshold of random formulas

- Computer ScienceRandom Struct. Algorithms
- 1998

In this work, in terms of the random formula, a decreasing sequence of random variables such that if the expected value of any one of them converges to zero, then is almost certainly unsatis able is considered.

Impact of Community Structure on SAT Solver Performance

- Computer ScienceSAT
- 2014

This paper provides evidence that the community structure of real-world SAT instances is correlated with the running time of CDCL SAT solvers, and shows that the number of communities and the Q value of the graph ofreal-world Sat instances is more predictive of theRunning time ofCDCL solvers than traditional metrics like number of variables or clauses.

Improving SAT Solvers by Exploiting Empirical Characteristics of CDCL

- Computer Science
- 2016

This thesis provides an unconventional perspective that CDCL solvers can solve real-world problems very efficiently and often more efficiently just by maintaining a small set of certain classes of learned clauses.

Selecting Complementary Pairs of Literals

- Mathematics, Computer ScienceElectron. Notes Discret. Math.
- 2003

The scaling window of the 2-SAT transition

- Mathematics, PhysicsRandom Struct. Algorithms
- 2001

Using this order parameter, it is proved that the 2-SAT phase transition is continuous with an order parameter critical exponent of 1 and the values of two other critical exponents are determined, showing that the exponents of 2- SAT are identical to those of the random graph.

Theory and Applications of Satisfiability Testing – SAT 2018

- Computer ScienceLecture Notes in Computer Science
- 2018

These approaches to analyze and understand the complexity of SAT are broadly discussed and it is suggested that their complementary perspectives could be useful in developing new models and answering questions that do not seem to be answerable by any individual approach.