Flexible and Generalized Uncertainty Optimization: Theory and Approaches by Weld

Description: Flexible and Generalized Uncertainty Optimization by Weldon A. Lodwick, Luiz L. Salles-Neto This book presents the theory and methods of flexible and generalized uncertainty optimization. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method. Back Cover This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method. Author Biography Weldon Alexander Lodwick is a Full Professor of Mathematics at the University of Colorado Denver. He holds a Ph.D. degree in mathematics (1980) from the Oregon State University. He is the co-editor of the book Fuzzy Optimization: Recent Developments and Applications, Studies in Fuzziness and Soft Computing Vol. 254, Springer-Verlag Berlin Heidelberg, 2010, and the author of the book Interval and Fuzzy Analysis: A Unified Approach in Advances in Imaging and Electronic Physics, Vol. 148, pp. 76–192, Elsevier, 2007. His current research interests include interval analysis, distance geometry, as well as flexible and generalized uncertainty optimization. Over the last thirty years he has taught applied mathematical modeling to undergraduate and graduate students, which covers topics such as radiation therapy of tumor, fuzzy and possibilistic optimization modeling, global optimization, optimal control, molecular distance geometry problems, and neural networks applied to control problems.Luiz L. Salles-Neto received the M.Sc. degree in mathematics and the Ph.D. degree in computational and applied mathematics from the University of Campinas, Brazil, in 2000 and 2005, respectively. He was a Research Scholar at the Universidad de Sevilla, Spain, in 2009/2010, and a Research Scholar at the University of Colorado Denver, USA, in 2017. He is an Associate Professor at Federal University of São Paulo, Brazil. Table of Contents An Introduction to Generalized Uncertainty Optimization.- Generalized Uncertainty Theory: A Language for Information Deficiency.- The Construction of Flexible and Generalized Uncertainty Optimization Input Data.- An Overview of Flexible and Generalized Uncertainty Optimization.- Flexible Optimization.- Generalized Uncertainty Optimization.- References. Feature Discusses how to analyze mathematically imprecise, uncertain, fuzzy information Shows how to construct input data for use in flexible and generalized uncertainty optimization problems Second edition enriched with more examples and a chapter on interval multi-objective mini-max regret theory Details ISBN3030611795 Author Luiz L. Salles-Neto Short Title Flexible and Generalized Uncertainty Optimization Series Studies in Computational Intelligence Language English Edition 2nd ISBN-10 3030611795 ISBN-13 9783030611798 Format Hardcover Series Number 696 DOI 10.1007/978-3-030-61180-4 Year 2021 Pages 193 Publication Date 2021-01-13 Publisher Springer Nature Switzerland AG Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Illustrations 30 Illustrations, color; 4 Illustrations, black and white; IX, 193 p. 34 illus., 30 illus. in color. UK Release Date 2021-01-13 Subtitle Theory and Approaches Edition Description 2nd ed. 2021 Replaces 9783319511054 Alternative 9783030611828 DEWEY 006.3 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:134122708;

Price: 182.78 AUD

Location: Melbourne

End Time: 2025-01-19T03:38:50.000Z

Shipping Cost: 9.35 AUD