Optimization von lange kenneth (25 Ergebnisse)

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Book contains pen & highlighter markings. In poor condition, suitable as a reading copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9780387203324.…

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9780387203324.…

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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Hardback. Zustand: New.

Zustand: Used. pp. xiii + 252.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Used. pp. xiii + 252.

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Zustand: New. 2016. Hardcover. . . . . . Books ship from the US and Ireland.

Zustand: New. In English.

Zustand: New. Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics. The author derives numerous algorithms from a broad diversity of application areas, with a particular e.

Buch. Zustand: Neu. Neuware - Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics. The author derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining.…

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Hardback. Zustand: New.

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Zustand: New.

Hardcover. Zustand: new. Hardcover. Offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can:Separate the variables of a problem. Avoid large matrix inversions. Linearize a problem. Restore symmetry.Deal with equality and inequality constraints gracefully. Turn a non-differentiable problem into a smooth problem.The author: Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics. Derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining.Summarizes a large amount of literature that has not reached book form before. Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics. The author derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Zustand: Neu. Optimization | Kenneth Lange | Taschenbuch | Springer Texts in Statistics | xvii | Englisch | 2015 | Springer | EAN 9781489992703 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New.

Paperback. Zustand: Brand New. 2nd edition. 546 pages. 9.25x6.10x1.18 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students' skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications.In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.…