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Titlebook: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming; Theory, Algorithms, Mohit Tawarmalani,Nikol

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書(shū)目名稱(chēng)Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming
副標(biāo)題Theory, Algorithms,
編輯Mohit Tawarmalani,Nikolaos V. Sahinidis
視頻videohttp://file.papertrans.cn/238/237855/237855.mp4
叢書(shū)名稱(chēng)Nonconvex Optimization and Its Applications
圖書(shū)封面Titlebook: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming; Theory, Algorithms,  Mohit Tawarmalani,Nikol
描述Interest in constrained optimization originated with the simple linear pro- gramming model since it was practical and perhaps the only computationally tractable model at the time. Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing. Modelers have, however, found the assumption of linearity to be overly restrictive in expressing the real-world phenomena and problems in economics, finance, business, communication, engineering design, computational biology, and other areas that frequently demand the use of nonlinear expressions and discrete variables in optimization models. Both of these extensions of the linear programming model are NP-hard, thus representing very challenging problems. On the brighter side, recent advances in algorithmic and computing technology make it possible to re- visit these problems with the hope of solving practically relevant problems in reasonable amounts of computational time. Initial attempts at solving nonlinear programs concentrated on the de- velopment of local optimization methods guarant
出版日期Book 2002
關(guān)鍵詞Partition; algorithm; algorithms; global optimization; linear optimization; model; nonlinear optimization;
版次1
doihttps://doi.org/10.1007/978-1-4757-3532-1
isbn_softcover978-1-4419-5235-6
isbn_ebook978-1-4757-3532-1Series ISSN 1571-568X
issn_series 1571-568X
copyrightSpringer Science+Business Media Dordrecht 2002
The information of publication is updating

書(shū)目名稱(chēng)Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming影響因子(影響力)




書(shū)目名稱(chēng)Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming被引頻次




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書(shū)目名稱(chēng)Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming年度引用




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書(shū)目名稱(chēng)Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming讀者反饋




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Relaxations of Factorable Programs, relaxations can be exponential in the number of variables. In this chapter, we present a slightly modified version of the factorable programming technique due to McCormick (1976) that, when used in conjunction with our relaxation techniques, constructs relaxations that are tight as well as manageab
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Domain Reduction, a global optimum. Domain reduction is also referred to as bounds tightening, domain contraction, and range reduction. Various techniques for domain reduction have been developed by Mangasarian & McLinden (1985), Thakur (1990), Hansen, Jaumard & Lu (1991), Hamed & McCormick (1993), Lamar (1993), Sav
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Miscellaneous Problems,rograms, indefinite quadratic programs, linear multiplicative programs, univariate polynomial programs, and benchmark problems from diverse application areas. All problems were solved to global optimality with an absolute tolerance of 10. unless otherwise specified. Our computational experience demo
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