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Titlebook: Linear Optimization Problems with Inexact Data; M. Fiedler,J. Nedoma,K. Zimmermann Book 2006 Springer-Verlag US 2006 Operations Research.O

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發(fā)表于 2025-3-21 18:13:05 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Linear Optimization Problems with Inexact Data
編輯M. Fiedler,J. Nedoma,K. Zimmermann
視頻videohttp://file.papertrans.cn/587/586366/586366.mp4
概述Presents a unified approach to solving linear programming problems with inexact data.Includes supplementary material:
圖書封面Titlebook: Linear Optimization Problems with Inexact Data;  M. Fiedler,J. Nedoma,K. Zimmermann Book 2006 Springer-Verlag US 2006 Operations Research.O
描述.Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems—for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average" values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the
出版日期Book 2006
關(guān)鍵詞Operations Research; Optimization Theory; Systems of interval linear equations; Systems of interval lin
版次1
doihttps://doi.org/10.1007/0-387-32698-7
isbn_softcover978-1-4419-4094-0
isbn_ebook978-0-387-32698-6
copyrightSpringer-Verlag US 2006
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J. Rohnr mapping them to real hardware;.Provides a first comprehensive introduction into design automation for quantum computing that tackles practically relevant tasks;.Targets the quantum computing community as well as the design automation community, showing both perspectives to quantum computing, and w
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oped the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the 978-1-4419-4094-0978-0-387-32698-6
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Solvability of systems of interval linear equations and inequalities,
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first comprehensive introduction into design automation for.This book offers readers an easy introduction into quantum computing as well as into the design for corresponding devices. The authors cover several design tasks which are important for quantum computing and introduce corresponding solutio
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M. Fiedler first comprehensive introduction into design automation for.This book offers readers an easy introduction into quantum computing as well as into the design for corresponding devices. The authors cover several design tasks which are important for quantum computing and introduce corresponding solutio
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J. Rohndesign tasks which are important for quantum computing and introduce corresponding solutions. A special feature of the book is that those tasks and solutions are explicitly discussed from a design automation perspective, i.e., utilizing clever algorithms and data structures which have been developed
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https://doi.org/10.1007/0-387-32698-7Operations Research; Optimization Theory; Systems of interval linear equations; Systems of interval lin
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