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020 _a9783319918389
040 _cIIT Kanpur
041 _aeng
082 _a658.4032
_bEx84
245 _aExtensions of dynamic programming for combinatorial optimization and data mining
_cHassan AbouEisha ...[et al.]
260 _aSwitzerland
_bSpringer
_c2019
300 _axvi, 280p
440 _aIntelligent systems reference library
490 _a/ edited by Janusz Kacprzyk; v.146
520 _aDynamic 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
650 _aMulti stage optimization
650 _aPareto optimal
700 _aAbouEisha, Hassan
700 _aAmin, Talha
700 _aChikalov, Igor
700 _aHussain, Shahid
700 _aMoshkov, Mikhail
942 _cBK
999 _c559684
_d559684