000 03315nam a22004935i 4500
001 978-0-387-74740-8
003 DE-He213
005 20161121231208.0
007 cr nn 008mamaa
008 100301s2008 xxu| s |||| 0|eng d
020 _a9780387747408
_9978-0-387-74740-8
024 7 _a10.1007/978-0-387-74740-8
_2doi
050 4 _aQA402.5-402.6
072 7 _aPBU
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519.6
_223
100 1 _aZhigljavsky, Anatoly.
_eauthor.
245 1 0 _aStochastic Global Optimization
_h[electronic resource] /
_cby Anatoly Zhigljavsky, Antanas Žilinskas.
264 1 _aBoston, MA :
_bSpringer US,
_c2008.
300 _aX, 262 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Optimization and Its Applications,
_x1931-6828 ;
_v9
505 0 _aBasic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions.
520 _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.
650 0 _aMathematics.
650 0 _aMathematical optimization.
650 0 _aProbabilities.
650 0 _aStatistics.
650 1 4 _aMathematics.
650 2 4 _aOptimization.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistical Theory and Methods.
700 1 _aŽilinskas, Antanas.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387740225
830 0 _aSpringer Optimization and Its Applications,
_x1931-6828 ;
_v9
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-74740-8
912 _aZDB-2-SMA
950 _aMathematics and Statistics (Springer-11649)
999 _c509958
_d509958