Commit 0d4cff6d authored by Remi Oudin's avatar Remi Oudin
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Update bibtex for RNA folding in SAT formulae

parent fc9fe08a
......@@ -120,7 +120,24 @@
pages={1280–1286},
year={2016},
abstract={
It is well established that in many scenarios there is no single solver that will provide optimal performance across a wide range of problem instances. Taking ad- vantage of this observation, research into algorithm se- lection is designed to help identify the best approach for each problem at hand. This segregation is usually based on carefully constructed features, designed to quickly present the overall structure of the instance as a constant size numeric vector. Based on these features, a plethora of machine learning techniques can be utilized to pre- dict the appropriate solver to execute, leading to sig- nificant improvements over relying solely on any one solver. However, being manually constructed, the cre- ation of good features is an arduous task requiring a great deal of knowledge of the problem domain of in- terest. To alleviate this costly yet crucial step, this paper presents an automated methodology for producing an informative set of features utilizing a deep neural net- work. We show that the presented approach completely automates the algorithm selection pipeline and is able to achieve significantly better performance than a sin- gle best solver across multiple problem domains.},
It is well established that in many scenarios there is no single solver that
will provide optimal performance across a wide range of problem
instances. Taking ad- vantage of this observation, research into
algorithm se- lection is designed to help identify the best approach for
each problem at hand. This segregation is usually based on carefully
constructed features, designed to quickly present the overall structure
of the instance as a constant size numeric vector. Based on these
features, a plethora of machine learning techniques can be utilized to
pre- dict the appropriate solver to execute, leading to sig- nificant
improvements over relying solely on any one solver. However, being
manually constructed, the cre- ation of good features is an arduous task
requiring a great deal of knowledge of the problem domain of in- terest.
To alleviate this costly yet crucial step, this paper presents an
automated methodology for producing an informative set of features
utilizing a deep neural net- work. We show that the presented approach
completely automates the algorithm selection pipeline and is able to
achieve significantly better performance than a sin- gle best solver
across multiple problem domains.},
}
......@@ -135,8 +152,25 @@
year={2009}
}
@article{GNNandSat,
author = {Benedikt Bünz and
Matthew Lamm},
title = {Graph Neural Network and Boolean Satisfiability},
@Inbook{Ganesh2012,
author="Ganesh, Vijay
and O'Donnell, Charles W.
and Soos, Mate
and Devadas, Srinivas
and Rinard, Martin C.
and Solar-Lezama, Armando",
editor="Cimatti, Alessandro
and Sebastiani, Roberto",
title="Lynx: A Programmatic SAT Solver for the RNA-Folding Problem",
bookTitle="Theory and Applications of Satisfiability Testing -- SAT 2012:
15th International Conference, Trento, Italy, June 17-20, 2012.
Proceedings",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="143--156",
isbn="978-3-642-31612-8",
doi="10.1007/978-3-642-31612-8_12",
url="http://dx.doi.org/10.1007/978-3-642-31612-8_12"
}
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