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Titlebook: Global Optimization with Non-Convex Constraints; Sequential and Paral Roman G. Strongin,Yaroslav D. Sergeyev Book 2000 Springer Science+Bus

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发表于 2025-3-21 16:40:20 | 显示全部楼层 |阅读模式
书目名称Global Optimization with Non-Convex Constraints
副标题Sequential and Paral
编辑Roman G. Strongin,Yaroslav D. Sergeyev
视频video
丛书名称Nonconvex Optimization and Its Applications
图书封面Titlebook: Global Optimization with Non-Convex Constraints; Sequential and Paral Roman G. Strongin,Yaroslav D. Sergeyev Book 2000 Springer Science+Bus
描述Everything should be made as simple as possible, but not simpler. (Albert Einstein, Readers Digest, 1977) The modern practice of creating technical systems and technological processes of high effi.ciency besides the employment of new principles, new materials, new physical effects and other new solutions ( which is very traditional and plays the key role in the selection of the general structure of the object to be designed) also includes the choice of the best combination for the set of parameters (geometrical sizes, electrical and strength characteristics, etc.) concretizing this general structure, because the Variation of these parameters ( with the structure or linkage being already set defined) can essentially affect the objective performance indexes. The mathematical tools for choosing these best combinations are exactly what is this book about. With the advent of computers and the computer-aided design the pro­ bations of the selected variants are usually performed not for the real examples ( this may require some very expensive building of sample op­ tions and of the special installations to test them ), but by the analysis of the corresponding mathematical models. The soph
出版日期Book 2000
关键词Computer; STATISTICA; algorithms; computer science; global optimization; mathematics; optimization; science
版次1
doihttps://doi.org/10.1007/978-1-4615-4677-1
isbn_softcover978-1-4613-7117-5
isbn_ebook978-1-4615-4677-1Series ISSN 1571-568X
issn_series 1571-568X
copyrightSpringer Science+Business Media Dordrecht 2000
The information of publication is updating

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https://doi.org/10.1007/978-1-4615-4677-1Computer; STATISTICA; algorithms; computer science; global optimization; mathematics; optimization; science
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Parallel Global Optimization Algorithms and Evaluation of the Efficiency of ParallelismThe general global optimization problem of finding a global minimizer .* and the global minimum .(.*) of the multiextremal function .(.) defined over a domain M, i.e., ., arises in different applications and numerical methods are used to find .-optimal solutions to this problem.
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Global Optimization under Non-Convex Constraints — The Index ApproachConsider the constrained global minimization problem . where the objective function ., henceforth denoted ..., i.e., ...(.) = .(.), and left-hand sides .., 1 ≤ . ≤ ., of the constraints are assumed to be Lipschitzian respectively with constants .., 1 ≤ . ≤ . + 1, and, in general, are multi-extremal.
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