Introduction to Applied Optimization by Urmila M. Diwekar (English) Paperback Bo

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Linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control, and stochastic optimal control are introduced in each self-contained chapter, with exercises, examples, and case studies, the true gems of this text.

FORMATPaperback LANGUAGEEnglish CONDITIONBrand New Publisher Description

Intended for advanced undergraduate/graduate students as well as scientists and engineers, this textbook presents a multi-disciplinary view of optimization, providing a thorough examination of algorithms, methods, techniques, and tools from diverse areas of optimization. Linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control, and stochastic optimal control are introduced in each self-contained chapter, with exercises, examples, and case studies, the true gems of this text. This third edition includes additional content in each chapter designed to clarify or enhance the exposition, and update methodologies and solutions. A new real-world case study related to sustainability is added in Chapters 2—7. GAMS, AIMMS, and MATLAB® files of case studies for Chapters 2, 3, 4, 5, and 7 are freely accessible electronically as extra source materials. A solutions manual is available to instructors who adopt the textbook for their course.From the reviews:This work is definitely a welcome addition to the existing optimization literature, given its emphasis on modeling and solution practice, as well as its 'user-friendly' style of exposition. — János D. Pintér, European Journal of Operations Research, Vol. 177, 2007 Urmila Diwekar's book on applied optimization is one of the few books on the subject that combines impressive breadth of coverage with delightful readability. In her exposition of concepts and algorithms in the major areas of optimization, she always goes to the heart of the matter and illustrates her explanations with simple diagrams and numerical examples. Graduate and undergraduate students, who constitute part of the target audience, should find this a very useful book. — Jamshed A. Modi, Interfaces, Vol.36 (1), 2006Optimization is a rich field with a strong history; this book nicely introduces both, moving from very introductory material to challenging techniques toward the end … Examples range from quite simplistic through realistic difficult scheduling problems. Some examples resurface in different chapter with twists to demonstrate how different techniques are required for differing data and constraints. — CHOICE, September 2004

Back Cover

Intended for advanced undergraduate/graduate students as well as scientists and engineers, this textbook presents a multi-disciplinary view of optimization, providing a thorough examination of algorithms, methods, techniques, and tools from diverse areas of optimization. Linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control, and stochastic optimal control are introduced in each self-contained chapter, with exercises, examples, and case studies, the true gems of this text. This third edition includes additional content in each chapter designed to clarify or enhance the exposition, and update methodologies and solutions. A new real-world case study related to sustainability is added in Chapters 2--7. GAMS, AIMMS, and MATLAB

Author Biography

Urmila Diwekar is the president of the Vishwamitra Research, a non-profit research institute that she founded to pursue multidisciplinary research in the areas of Optimization under Uncertainty and Computer-aided Design applied to Energy, Environment, and Sustainability.  From 2002-2004, she was a Professor in the Departments of Chemical Engineering, Bio-Engineering, and Industrial Engineering, and in the Institute for Environmental Science and Policy, at the University of Illinois at Chicago (UIC).  She has a special formal arrangement with UIC where remains as the main advisor for her Ph.D. and M.S. students and teaches a course on optimization.  From 1991-2002 she was on the faculty of the Carnegie Mellon University, with early promotions to both the Associate and the Full Professor level.

Table of Contents

1. Introduction.- 2. Linear Programming.- 3. Nonlinear Programming.-4. Discrete Optimization.- 5. Optimization Under Uncertainty.- 6. Multiobjective Optimization.- 7.Optimal Control and Dynamic Optimization.- Index.

Long Description

Optimization has pervaded all spheres of human endeavor. Although op- mization has been practiced in some form or other from the early prehistoric era, this area has seen progressive growth during the last ?ve decades. M- ern society lives not only in an environment of intense competition but is also constrained to plan its growth in a sustainable manner with due concern for conservation of resources. Thus, it has become imperative to plan, design, operate, and manage resources and assets in an optimal manner. Early - proaches have been to optimize individual activities in a standalone manner, however,thecurrenttrendistowardsanintegratedapproach:integratings- thesis and design, design and control, production planning, scheduling, and control. The functioning of a system may be governed by multiple perf- mance objectives. Optimization of such systems will call for special strategies for handling the multiple objectives to provide solutions closer to the systems requirement. Uncertainty and variability are two issues which render op- mal decision making di'cult. Optimization under uncertainty would become increasingly important if one is to get the best out of a system plagued by uncertain components. These issues have thrown up a large number of ch- lenging optimization problems which need to be resolved with a set of existing and newly evolving optimization tools. Optimization theory had evolved initially to provide generic solutions to optimizationproblemsinlinear,nonlinear,unconstrained,andconstrained- mains. Theseoptimization problems wereoften called mathematical progr- mingproblemswithtwodistinctiveclassi'cations,namelylinearandnonlinear programming problems.

Feature

Includes problem sets and exercises Introduces applied optimization with unique applications Solutions manual available upon adoptions

Details ISBN3030554066 Author Urmila M. Diwekar Pages 358 Series Springer Optimization and Its Applications Language English Year 2021 Edition 3rd ISBN-10 3030554066 ISBN-13 9783030554064 Format Paperback DEWEY 519.6 Series Number 22 Publisher Springer Nature Switzerland AG Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Publication Date 2021-10-30 UK Release Date 2021-10-30 Illustrations 20 Illustrations, color; 92 Illustrations, black and white; XXIX, 358 p. 112 illus., 20 illus. in color. Edited by Jordina Torrents Barrena Birth 1945 Affiliation Georgia Institute of Technology Position Kranzberg Professor Qualifications SJ Edition Description 3rd ed. 2020 Alternative 9783030554033 Audience Professional & Vocational We've got this

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