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    Extensions of dynamic programming for combinatorial optimization and data mining

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    Type
    Book
    Authors
    AbouEisha, Hassan M. cc
    Amin, Talha M. cc
    Chikalov, Igor
    Hussain, Shahid cc
    Moshkov, Mikhail cc
    KAUST Department
    Academic Affairs
    Applied Mathematics and Computational Science Program
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
    Office of the VP
    Date
    2018-05-22
    Permanent link to this record
    http://hdl.handle.net/10754/665493
    
    Metadata
    Show full item record
    Abstract
    Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.
    Citation
    AbouEisha, H., Amin, T., Chikalov, I., Hussain, S., & Moshkov, M. (2019). Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining. Intelligent Systems Reference Library. doi:10.1007/978-3-319-91839-6
    Publisher
    Springer Nature
    ISBN
    9783319918389
    9783319918396
    DOI
    10.1007/978-3-319-91839-6
    Additional Links
    http://link.springer.com/10.1007/978-3-319-91839-6
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-91839-6
    Scopus Count
    Collections
    Applied Mathematics and Computational Science Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Books

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