000 | 03315nam a22004935i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-0-387-74740-8 _2doi |
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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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |