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Stability, Approximation, and Decomposition in Two- and Multistage Stochastic Programming, Christian K?chler


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Автор: Christian K?chler
Название:  Stability, Approximation, and Decomposition in Two- and Multistage Stochastic Programming
ISBN: 9783834809216
Издательство: Springer
Классификация:
ISBN-10: 3834809217
Обложка/Формат: Paperback
Страницы: 184
Вес: 0.23 кг.
Дата издания: 2009
Серия: Stochastic Programming
Язык: English
Иллюстрации: 49 black & white illustrations, 9 black & white tables, biography
Размер: 210 x 148 x 10
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Christian Kuchler studies various aspects of the stability of stochastic optimization problems as well as approximation and decomposition methods in stochastic programming. In particular, the author presents an extension of the Nested Benders decomposition algorithm related to the concept of recombining scenario trees.


Scattered Data Approximation

Автор: Holger Wendland
Название: Scattered Data Approximation
ISBN: 0521843359 ISBN-13(EAN): 9780521843355
Издательство: Cambridge Academ
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Цена: 17424.00 р.
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Описание: A complete self-contained introduction to the theory of scattered data approximation. Written with graduates and researchers in mind, the text brings together much of the necessary background material into a single treatment and provides students with complete proofs to the theory developed within.

Approximation Methods in Probability Theory

Автор: ?ekanavi?ius
Название: Approximation Methods in Probability Theory
ISBN: 3319340719 ISBN-13(EAN): 9783319340715
Издательство: Springer
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Цена: 7965.00 р.
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Описание: This book presents a wide range of well-known and less common methods used for estimating the accuracy of probabilistic approximations, including the Esseen type inversion formulas, the Stein method as well as the methods of convolutions and triangle function. Emphasising the correct usage of the methods presented, each step required for the proofs is examined in detail. As a result, this textbook provides valuable tools for proving approximation theorems.While Approximation Methods in Probability Theory will appeal to everyone interested in limit theorems of probability theory, the book is particularly aimed at graduate students who have completed a standard intermediate course in probability theory. Furthermore, experienced researchers wanting to enlarge their toolkit will also find this book useful.

Yosida Approximations of Stochastic Differential Equations in Infinite Dimensions and Applications

Автор: Govindan
Название: Yosida Approximations of Stochastic Differential Equations in Infinite Dimensions and Applications
ISBN: 3319456822 ISBN-13(EAN): 9783319456829
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This research monograph brings together, for the first time, the varied literature on Yosida approximations of stochastic differential equations (SDEs) in infinite dimensions and their applications into a single cohesive work. The author provides a clear and systematic introduction to the Yosida approximation method and justifies its power by presenting its applications in some practical topics such as stochastic stability and stochastic optimal control. The theory assimilated spans more than 35 years of mathematics, but is developed slowly and methodically in digestible pieces.The book begins with a motivational chapter that introduces the reader to several different models that play recurring roles throughout the book as the theory is unfolded, and invites readers from different disciplines to see immediately that the effort required to work through the theory that follows is worthwhile. From there, the author presents the necessary prerequisite material, and then launches the reader into the main discussion of the monograph, namely, Yosida approximations of SDEs, Yosida approximations of SDEs with Poisson jumps, and their applications. Most of the results considered in the main chapters appear for the first time in a book form, and contain illustrative examples on stochastic partial differential equations. The key steps are included in all proofs, especially the various estimates, which help the reader to get a true feel for the theory of Yosida approximations and their use.This work is intended for researchers and graduate students in mathematics specializing in probability theory and will appeal to numerical analysts, engineers, physicists and practitioners in finance who want to apply the theory of stochastic evolution equations. Since the approach is based mainly in semigroup theory, it is amenable to a wide audience including non-specialists in stochastic processes.

Interpolation and Extrapolation Optimal Designs V1: Polynomial Regression and Approximation Theory

Автор: Giorgio Celant, Michel Broniatowski
Название: Interpolation and Extrapolation Optimal Designs V1: Polynomial Regression and Approximation Theory
ISBN: 1848219954 ISBN-13(EAN): 9781848219953
Издательство: Wiley
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Цена: 22010.00 р.
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Описание:

This book is the first of a series which focuses on the interpolation and extrapolation of optimal designs, an area with significant applications in engineering, physics, chemistry and most experimental fields.

In this volume, the authors emphasize the importance of problems associated with the construction of design. After a brief introduction on how the theory of optimal designs meets the theory of the uniform approximation of functions, the authors introduce the basic elements to design planning and link the statistical theory of optimal design and the theory of the uniform approximation of functions.

The appendices provide the reader with material to accompany the proofs discussed throughout the book.

Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition

Автор: Haruo Yanai; Kei Takeuchi; Yoshio Takane
Название: Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition
ISBN: 1461428599 ISBN-13(EAN): 9781461428596
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This valuable reference on projectors, generalized inverses, and SVD covers concepts numerous cutting-edge concepts and provides systematic and in-depth accounts of these ideas from the viewpoint of linear transformations of finite dimensional vector spaces.


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