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Titlebook: Smooth Nonlinear Optimization in Rn; Tamás Rapcsák Book 1997 Springer Science+Business Media Dordrecht 1997 Optimality Conditions.Optimiza

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書(shū)目名稱Smooth Nonlinear Optimization in Rn
編輯Tamás Rapcsák
視頻videohttp://file.papertrans.cn/870/869156/869156.mp4
叢書(shū)名稱Nonconvex Optimization and Its Applications
圖書(shū)封面Titlebook: Smooth Nonlinear Optimization in Rn;  Tamás Rapcsák Book 1997 Springer Science+Business Media Dordrecht 1997 Optimality Conditions.Optimiza
描述Experience gained during a ten-year long involvement in modelling, program- ming and application in nonlinear optimization helped me to arrive at the conclusion that in the interest of having successful applications and efficient software production, knowing the structure of the problem to be solved is in- dispensable. This is the reason why I have chosen the field in question as the sphere of my research. Since in applications, mainly from among the nonconvex optimization models, the differentiable ones proved to be the most efficient in modelling, especially in solving them with computers, I started to deal with the structure of smooth optimization problems. The book, which is a result of more than a decade of research, can be equally useful for researchers and stu- dents showing interest in the domain, since the elementary notions necessary for understanding the book constitute a part of the university curriculum. I in- tended dealing with the key questions of optimization theory, which endeavour, obviously, cannot bear all the marks of completeness. What I consider the most crucial point is the uniform, differential geometric treatment of various questions, which provides the r
出版日期Book 1997
關(guān)鍵詞Optimality Conditions; Optimization algorithm; Optimization algorithms; algorithms; linear optimization;
版次1
doihttps://doi.org/10.1007/978-1-4615-6357-0
isbn_softcover978-1-4613-7920-1
isbn_ebook978-1-4615-6357-0Series ISSN 1571-568X
issn_series 1571-568X
copyrightSpringer Science+Business Media Dordrecht 1997
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Variable Metric Methods Along Geodesics,ions, converges to a solution. Thus, an algorithm is said to be globally convergent if for arbitrary starting points , the algorithm is guaranteed to generate a sequence of points converging to a solution. Many of the most important algorithms for solving nonlinear optimization problems are not glob
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An Improvement of the Lagrange Multiplier Rule for Smooth Optimization Problems, this theory.” Here, an improvement of the sufficiency part of the Lagrange multiplier rule is presented. That is to say that a result stronger than that of Lagrange, with respect to the sufficiency part, can be proved under the same type of conditions he used, based on a different, perhaps a deeper
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Tamás Rapcsáktion contained in these images?.In this chapter, I review some broad applications and uses of imagery for ecologists and wildlife biologists. Images can be used to (1) identify species for occurrence, (2) identify individuals for mark-recapture studies and other behavioral studies, and (3) count ind
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