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The satisfiability threshold for randomly generated binary constraint satisfaction problems
2006
Random structures & algorithms (Print)
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We study two natural models of randomly generated constraint satisfaction problems. ...
We determine how quickly the domain size must grow with n to ensure that these models are robust in the sense that they exhibit a non-trivial threshold of satisfiability, and we determine the asymptotic ...
Introduction The Constraint Satisfaction Problem (CSP) is a broadly studied generalization of k-SAT. ...
doi:10.1002/rsa.20118
fatcat:zbvvznt7b5gt3musk2mjaupbf4
The Satisfiability Threshold for Randomly Generated Binary Constraint Satisfaction Problems
[chapter]
2003
Lecture Notes in Computer Science
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We study two natural models of randomly generated constraint satisfaction problems. ...
We determine how quickly the domain size must grow with n to ensure that these models are robust in the sense that they exhibit a non-trivial threshold of satisfiability, and we determine the asymptotic ...
Introduction The Constraint Satisfaction Problem (CSP) is a broadly studied generalization of k-SAT. ...
doi:10.1007/978-3-540-45198-3_24
fatcat:uhvyu3miu5dspo277jnpe25jke
Page 7844 of Mathematical Reviews Vol. , Issue 2003j
[page]
2003
Mathematical Reviews
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O.] (3-TRNT-C; Toronto, ON) A probabilistic analysis of randomly generated binary constraint satisfaction problems. Theoret. Comput. Sci. 290 (2003), no. 3, 1815-1828. ...
Summary: “Solving non-binary constraint satisfaction problems, a crucial challenge today, can be tackled in two different ways: translating the non-binary problem into an equivalent binary one, or extending ...
NAIS: A Calibrated Immune Inspired Algorithm to Solve Binary Constraint Satisfaction Problems
[chapter]
2007
Lecture Notes in Computer Science
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The tests were carried out using random generated binary constraint satisfaction problems on the transition phase where are the hardest problems. ...
We have calibrated our algorithm using Relevance Estimation and Value Calibration (REVAC), that is a new technique, recently introduced to find the parameter values for evolutionary algorithms. ...
Binary Constraint Satisfaction Problems For simplicity we restrict our attention here to binary CSPs, where the constraints involve two variables. Binary constraints are binary relations. ...
doi:10.1007/978-3-540-73922-7_3
fatcat:lib3pdgiardshh5dt42shk6qcu
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
2007
Artificial Intelligence
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Li, Exact phase transitions in random constraint satisfaction problems, Journal of Artificial Intelligence Research 12 (2000) 93-103; K. Xu, W. ...
In that case, a threshold point can be precisely located and all instances have the guarantee to be hard at the threshold, i.e., to have an exponential tree-resolution complexity. ...
Acknowledgements We would like to thank the AIJ anonymous referees for their helpful comments and suggestions. ...
doi:10.1016/j.artint.2007.04.001
fatcat:ru3xqka5vff7rbn2tesiptyosm
Towards Effective Deep Learning for Constraint Satisfaction Problems
[chapter]
2018
Lecture Notes in Computer Science
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Many attempts have been made to apply machine learning techniques to constraint satisfaction problems (CSPs). However, none of them have made use of the recent advances in deep learning. ...
To the best of our knowledge, this is the first effective application of deep learning to CSPs that yields >99.99% prediction accuracy on random Boolean binary CSPs whose constraint tightnesses or constraint ...
The research at the University of Southern California (USC) was supported by National Science Foundation (NSF) under grant numbers 1724392, 1409987, and 1319966. ...
doi:10.1007/978-3-319-98334-9_38
fatcat:djqet76n4bf6jdqebtioh5ik4m
Stochastic Constraint Programming
[article]
2009
arXiv
pre-print
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They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. ...
To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. ...
If the fraction of the resulting constraint satisfaction problems that are satisfiable is at least equal to the threshold θ, then the original stochastic constraint satisfaction problem is likely to be ...
arXiv:0903.1152v1
fatcat:lyq6id4uefb6lojov435u3jcnm
Constraint satisfaction problems and neural networks: A statistical physics perspective
2009
Journal of Physiology - Paris
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On the other hand some crucial inference problems in neurobiology, like those generated in multi-electrode recordings, naturally translate into hard constraint satisfaction problems. ...
In particular, the use of cutting edge statistical physics concepts and methods allow one to solve very large constraint satisfaction problems like random satisfiability, coloring, or error correction. ...
Conclusion The message passing strategy often provides the most efficient algorithms for solving hard constraint satisfaction problems, or for inference in graphical models. ...
doi:10.1016/j.jphysparis.2009.05.013
pmid:19616623
fatcat:ajpvvpa3ujg5tgay6jpvrmh5z4
Constraint satisfaction problems and neural networks: a statistical physics perspective
[article]
2008
arXiv
pre-print
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On the other hand some crucial inference problems in neurobiology, like those generated in multi-electrode recordings, naturally translate into hard constraint satisfaction problems. ...
In particular, the use of cutting edge statistical physics concepts and methods allow one to solve very large constraint satisfaction problems like random satisfiability, coloring, or error correction. ...
Conclusion The message passing strategy often provides the most efficient algorithms for solving hard constraint satisfaction problems, or for inference in graphical models. ...
arXiv:0803.3061v1
fatcat:wadx7u6k6ralpgiypkimq4a2fu
Lossy Data Compression with Random Gates
2005
Physical Review Letters
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algorithm for constraint satisfaction problems derived from statistical physics. ...
We introduce a new protocol for a lossy data compression algorithm which is based on constraint satisfaction gates. ...
algorithm for constraint satisfaction problems derived from statistical physics. ...
doi:10.1103/physrevlett.95.038701
pmid:16090781
fatcat:expaem5gcjclzc6krbgt3yehiq
Towards the Patterns of Hard CSPs with Association Rule Mining
[article]
2009
arXiv
pre-print
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The hardness of finite domain Constraint Satisfaction Problems (CSPs) is a very important research area in Constraint Programming (CP) community. ...
patterns of the hardness of the randomly generated CSPs ...
Constraint Satisfaction Problems A binary Constraint Satisfaction Problem (BCSP) is a CSP that satisfies the above (i) and (ii); the only difference is the constraint set C. ...
arXiv:0906.5040v1
fatcat:um5krrpvvbb6bm3kaudcfeh6cy
Sharp Thresholds for a Random Constraint Satisfaction Problem
2017
Open Journal of Applied Sciences
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The phenomenon of phase transition in constraint satisfaction problems (CSPs) plays a crucial role in the field of artificial intelligence and computational complexity theory. ...
The randomly selected constraints constitute a random CSP instance. An assignment that satisfies all the constraints simultaneously is called a solution of the CSP instance. ...
Many problems in the fields of artificial intelligence, computer science and automatic control can be modeled as constraint satisfaction problems. ...
doi:10.4236/ojapps.2017.710041
fatcat:kywg5uyedba5fpnagicr22l3uy
A heuristic incremental modeling approach to course timetabling
[chapter]
1998
Lecture Notes in Computer Science
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Speci cally, w e consider how a timetabling problem can be represented as a Constraint Satisfaction Problem (CSP), and propose an algorithm for its solution which i m p r o ves upon the basic idea of backtracking ...
In this paper the application of constraint-based reasoning to timetable generation is examined. ...
Satisfying all of the constraints does not guarantee 100% students satisfaction. ...
doi:10.1007/3-540-64575-6_37
fatcat:625wqwoq2bd3tg7lecd4s2kkiu
A self-adaptive differential evolution algorithm for binary CSPs
2011
Computers and Mathematics with Applications
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Attention is concentrated on varying F and CR dynamically with each generation evolution. SADE maintains the diversity of population and improves the global convergence ability. ...
In order to balance an individual's exploration and exploitation capability for different evolving phases, F and CR are equal to two different self-adjusted nonlinear functions. ...
We start with the necessary background on Constraint Satisfaction Problems and DE algorithms. In Section 3, we proposed SADE for binary CSPs. ...
doi:10.1016/j.camwa.2011.06.053
fatcat:5k4k2xfywrckxcknn4bapm4jfi
On the Behavior and Application of Constraint Weighting
[chapter]
1999
Lecture Notes in Computer Science
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We extend previous results from satisfiability testing by looking at the broader domain of constraint satisfaction and test for differences in performance using randomly generated problems and problems ...
We find constraint weighting produces fairly consistent behaviour within problem domains, and is more influenced by the number and interconnectedness of constraints than the realism or randomness of a ...
In addition we run tests on the well-studied problem of random binary constraint satisfaction [10] . For the purpose of the research, a university timetable problem generator was developed. ...
doi:10.1007/978-3-540-48085-3_32
fatcat:sedmnx3thvgirpbwa2lnp7anoy
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