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Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 16th International C Louis-Martin Rousseau,Kostas

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书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research
副标题16th International C
编辑Louis-Martin Rousseau,Kostas Stergiou
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 16th International C Louis-Martin Rousseau,Kostas
描述.This book constitutes the proceedings of the 16th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2019, held in Thessaloniki, Greece, in June 2019..The 34 full papers presented together with 9 short papers were carefully reviewed and selected from 94 submissions. The conference brings together interested researchers from Constraint Programming (CP), Artificial Intelligence (AI), and Operations Research (OR) to present new techniques or applications and to provide an opportunity for researchers in one area to learn about techniques in the others. A main objective of this conference series is also to give these researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems..
出版日期Conference proceedings 2019
关键词mathematical optimization; constraint programming; integer programming; satisfiability; combinatorial op
版次1
doihttps://doi.org/10.1007/978-3-030-19212-9
isbn_softcover978-3-030-19211-2
isbn_ebook978-3-030-19212-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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An Improved Subsumption Testing Algorithm for the Optimal-Size Sorting Network Problem,ing networks. We were able to generate all the complete sets of filters for comparator networks with 9 channels, confirming that the 25-comparators sorting network is optimal. The running time was reduced more than 10 times, compared to the state-of-the-art result described in [.].
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SAT Encodings of Pseudo-Boolean Constraints with At-Most-One Relations,cally reduced in size thanks to the presence of AMO constraints. Moreover, the new encodings preserve the propagation properties of the original ones. Our experiments show a significant reduction in solving time thanks to the new encodings.
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Constraint Programming for Dynamic Symbolic Execution of JavaScript,ch against state-of-the-art SMT solvers. Experimental results, in terms of both speed and coverage, show the benefits of our approach, thus opening new research vistas for using CP techniques in the service of program analysis.
intertwine
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Local Rapid Learning for Integer Programs,c criteria to predict the chance for local Rapid Learning to be successful. Our computational experiments indicate that our extended Rapid Learning algorithm significantly speeds up MIP search and is particularly beneficial on highly dual degenerate problems.
Insulin
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