Stochastic Optimization

This book PDF is perfect for those who love Computers genre, written by Johannes Schneider and published by Springer Science & Business Media which was released on 06 August 2007 with total hardcover pages 568. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Stochastic Optimization books below.

Stochastic Optimization

Author	: Johannes Schneider
File Size	: 46,7 Mb
Publisher	: Springer Science & Business Media
Language	: English
Release Date	: 06 August 2007
ISBN	: 9783540345602
Pages	: 568 pages

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Stochastic Optimization by Johannes Schneider Book PDF Summary

This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.

Stochastic Optimization by Johannes Schneider,Scott Kirkpatrick

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First order and Stochastic Optimization Methods for Machine Learning by Guanghui Lan

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and

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Convex and Stochastic Optimization by J. Frédéric Bonnans

This textbook provides an introduction to convex duality for optimization problems in Banach spaces, integration theory, and their application to stochastic programming problems in a static or dynamic setting. It introduces and analyses the main algorithms for stochastic programs, while the theoretical aspects are carefully dealt with. The reader is

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Stochastic Optimization by Stanislav Uryasev,Panos M. Pardalos

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to

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Multistage Stochastic Optimization by Georg Ch. Pflug,Alois Pichler

Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization.

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Stochastic Optimization Models in Finance by William T. Ziemba,Raymond G. Vickson

A reprint of one of the classic volumes on portfolio theory and investment, this book has been used by the leading professors at universities such as Stanford, Berkeley, and Carnegie-Mellon. It contains five parts, each with a review of the literature and about 150 pages of computational and review exercises and

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Introduction to Stochastic Programming by John R. Birge,François Louveaux

This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with

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Stochastic Optimization Methods by Kurt Marti

This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and

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