Entropy Optimization and Mathematical Programming

By: Shu-Cherng Fang, J. R. Rajasekera, H. S. J. Tsao

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Hardcover | 31 July 1997

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360 Pages

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24.77 x 17.15 x 3.18

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Entropy optimization is a useful combination of classical engineering theory (entropy) with mathematical optimization. The resulting entropy optimization models have proved their usefulness with successful applications in areas such as image reconstruction, pattern recognition, statistical inference, queuing theory, spectral analysis, statistical mechanics, transportation planning, urban and regional planning, input-output analysis, portfolio investment, information analysis, and linear and nonlinear programming.
While entropy optimization has been used in different fields, a good number of applicable solution methods have been loosely constructed without sufficient mathematical treatment. A systematic presentation with proper mathematical treatment of this material is needed by practitioners and researchers alike in all application areas. The purpose of this book is to meet this need. Entropy Optimization and Mathematical Programming offers perspectives that meet the needs of diverse user communities so that the users can apply entropy optimization techniques with complete comfort and ease. With this consideration, the authors focus on the entropy optimization problems in finite dimensional Euclidean space such that only some basic familiarity with optimization is required of the reader.

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You Can Find This Book In

Non-Fiction Business & Management Operational Research Reference, Information & Interdisciplinary Subjects Research & Information Information theory Mathematics Optimisation Linear Programming Applied Mathematics Probability & Statistics Calculus & Mathematical Analysis

Engineering & Technology Mechanical Engineering & Materials Production & Industrial Engineering

Preface
Introduction to Entropy and Entropy Optimization Principles	p. 1
Entropy Optimization Models	p. 17
Entropy Optimization Methods: Linear Case	p. 51
Entropy Optimization Methods: General Convex Case	p. 125
Entropic Perturbation Approach to Mathematical Programming	p. 187
L[subscript p]-Norm Perturbation Approach : A Generalization of Entropic Perturbation	p. 247
Extension and Related Results	p. 285
Bibliography	p. 325
Index	p. 341
Table of Contents provided by Blackwell. All Rights Reserved.

Entropy Optimization and Mathematical Programming

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Hardcover

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