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Titlebook: Convex Analysis and Nonlinear Optimization; Theory and Examples Jonathan M. Borwein,Adrian S. Lewis Textbook 20001st edition Springer Scien

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书目名称Convex Analysis and Nonlinear Optimization
副标题Theory and Examples
编辑Jonathan M. Borwein,Adrian S. Lewis
视频video
概述Reviews the increasingly sophisticated state of computational optimization techniques.Provides an accessible account of convex analysis and its applications and extensions.New Edition adds material on
丛书名称CMS Books in Mathematics
图书封面Titlebook: Convex Analysis and Nonlinear Optimization; Theory and Examples Jonathan M. Borwein,Adrian S. Lewis Textbook 20001st edition Springer Scien
描述Optimization is a rich and thriving mathematical discipline. The theory underlying current computational optimization techniques grows ever more sophisticated. The powerful and elegant language of convex analysis unifies much of this theory. The aim of this book is to provide a concise, accessible account of convex analysis and its applications and extensions, for a broad audience. It can serve as a teaching text, at roughly the level of first year graduate students. While the main body of the text is self-contained, each section concludes with an often extensive set of optional exercises. The new edition adds material on semismooth optimization, as well as several new proofs that will make this book even more self-contained.
出版日期Textbook 20001st edition
关键词Mathematica; Microsoft Access; PostScript; boundary element method; constraint; convex analysis; duality; e
版次1
doihttps://doi.org/10.1007/978-1-4757-9859-3
isbn_ebook978-1-4757-9859-3Series ISSN 1613-5237 Series E-ISSN 2197-4152
issn_series 1613-5237
copyrightSpringer Science+Business Media New York 2000
The information of publication is updating

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Voltammetric Methods in Brain Systemsonal vector space over the reals ., equipped with an inner product ‹·,·›. We would lose no generality if we considered only the space .. of real (column) .-vectors (with its standard inner product), but a more abstract, coordinate-free notation is often more flexible and elegant.
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Immunocytochemical Studies of the Retina,les: it gives the necessary optimality condition 0 ∈ ∂.(.) when the point . is a (local) minimizer (Proposition 3.1.5); it reduces to {∇.(.)} when . is differentiable at . (Corollary 3.1.10); and it often satisfies certain calculus rules such as ∂(. + .)(.) = ∂ .(.) + ∂ .(.) (Theorem 3.3.5). For a v
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Ian Zachary,Spiros Servos,Barbara HerrenWhile our list is far from complete it should help illuminate the places in which care is appropriate when “generalizing”. Many of our main results (on subgradients, variational principles, open mappings, Fenchel duality, metric regularity) immediately generalize to at least reflexive Banach spaces.
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https://doi.org/10.1385/0896033120Early in multivariate calculus we learn the significance of differentiability in finding minimizers. In this section we begin our study of the interplay between convexity and differentiability in optimality conditions.
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