LEADER 03681nam  2200661 a 450 
001 9910450933503321
005 20200520144314.0
010   $a1-281-13893-2
010   $a9786611138936
010   $a0-387-74740-0
024 7 $a10.1007/978-0-387-74740-8
035   $a(CKB)1000000000413587
035   $a(EBL)337204
035   $a(OCoLC)261324691
035   $a(SSID)ssj0000251545
035   $a(PQKBManifestationID)11200727
035   $a(PQKBTitleCode)TC0000251545
035   $a(PQKBWorkID)10170585
035   $a(PQKB)10159560
035   $a(DE-He213)978-0-387-74740-8
035   $a(MiAaPQ)EBC337204
035   $a(PPN)123733952
035   $a(Au-PeEL)EBL337204
035   $a(CaPaEBR)ebr10218002
035   $a(CaONFJC)MIL113893
035   $a(EXLCZ)991000000000413587
100   $a20080924d2008      uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aStochastic global optimization$b[electronic resource] /$fby Anatoly Zhigljavsky, Antanas Z?ilinskas
205   $a1st ed. 2008.
210   $aNew York $cSpringer$d2008
215   $a1 online resource (270 p.)
225 1 $aSpringer optimization and its applications ;$vv. 1
300   $aDescription based upon print version of record.
311   $a0-387-74022-8 
320   $aIncludes bibliographical references and index.
327   $aBasic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions.
330   $aThis book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; the topics include the basic principles and methods of global random search, statistical inference in random search, Markovian and population-based random search methods, methods based on statistical models of multimodal functions and principles of rational decisions theory. Key features: * Inspires readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods; * Includes a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms; * Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms; *Provides a thorough description of the methods based on statistical models of objective function; *Discusses criteria for evaluating efficiency of optimization algorithms and difficulties occurring in applied global optimization. Stochastic Global Optimization is intended for mature researchers and graduate students interested in global optimization, operations research, computer science, probability, statistics, computational and applied mathematics, mechanical and chemical engineering, and many other fields where methods of global optimization can be used.
410  0$aSpringer optimization and its applications ;$vv. 1.
606   $aMathematical optimization
606   $aStochastic processes
608   $aElectronic books.
615  0$aMathematical optimization.
615  0$aStochastic processes.
676   $a519.62
700   $aZhigli?avskii?$b A. A$g(Anatolii? Aleksandrovich)$0468354
701   $aZhilinskas$b A$0980486
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910450933503321
996   $aStochastic global optimization$92237169
997   $aUNINA