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170617s2017 xxu| s |||| 0|eng d |
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|a 9781493971992
|9 978-1-4939-7199-2
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|a 10.1007/978-1-4939-7199-2
|2 doi
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|a QA76.6-76.66
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|a Sergeyev, Yaroslav D.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 245 |
1 |
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|a Deterministic Global Optimization
|h [electronic resource] :
|b An Introduction to the Diagonal Approach /
|c by Yaroslav D. Sergeyev, Dmitri E. Kvasov.
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| 250 |
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|a 1st ed. 2017.
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| 264 |
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1 |
|a New York, NY :
|b Springer New York :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a X, 136 p. 39 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a SpringerBriefs in Optimization,
|x 2190-8354
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| 505 |
0 |
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|a 1. Lipschitz global optimization -- 2. One-dimensional algorithms and their accleration -- 3. Diagonal approach and efficient paritioning strategies -- 4. Global optimization algorithms based on the non-redundant partitions -- References.
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| 520 |
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|a This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field. Multiextremal continuous problems that have an unknown structure with Lipschitz objective functions and functions having the first Lipschitz derivatives defined over hyperintervals are examined. A class of algorithms using several Lipschitz constants is introduced which has its origins in the DIRECT (DIviding RECTangles) method. This new class is based on an efficient strategy that is applied for the search domain partitioning. In addition a survey on derivative free methods and methods using the first derivatives is given for both one-dimensional and multi-dimensional cases. Non-smooth and smooth minorants and acceleration techniques that can speed up several classes of global optimization methods with examples of applications and problems arising in numerical testing of global optimization algorithms are discussed. Theoretical considerations are illustrated through engineering applications. Extensive numerical testing of algorithms described in this book stretches the likelihood of establishing a link between mathematicians and practitioners. The authors conclude by describing applications and a generator of random classes of test functions with known local and global minima that is used in more than 40 countries of the world. This title serves as a starting point for students, researchers, engineers, and other professionals in operations research, management science, computer science, engineering, economics, environmental sciences, industrial and applied mathematics to obtain an overview of deterministic global optimization. .
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| 650 |
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|a Computer programming.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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|a Computer science—Mathematics.
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| 650 |
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|a Computer mathematics.
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| 650 |
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|a Programming Techniques.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I14010
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| 650 |
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|a Optimization.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M26008
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| 650 |
2 |
4 |
|a Mathematics of Computing.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I17001
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| 650 |
2 |
4 |
|a Mathematical Applications in Computer Science.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M13110
|
| 700 |
1 |
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|a Kvasov, Dmitri E.
|e author.
|0 (orcid)0000-0002-5067-4383
|1 https://orcid.org/0000-0002-5067-4383
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9781493971978
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781493971985
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| 830 |
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|a SpringerBriefs in Optimization,
|x 2190-8354
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4939-7199-2
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| 912 |
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|a ZDB-2-SMA
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| 912 |
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|a ZDB-2-SXMS
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| 950 |
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|a Mathematics and Statistics (SpringerNature-11649)
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| 950 |
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|a Mathematics and Statistics (R0) (SpringerNature-43713)
|
