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Introduction To Applied Optimization


Introduction To Applied Optimization
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Introduction To Applied Optimization


Introduction To Applied Optimization
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Author : Urmila Diwekar
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Introduction To Applied Optimization written by Urmila Diwekar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.


Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting; Introduces applied optimization to the hazardous waste blending problem; Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control; Includes an extensive bibliography at the end of each chapter and an index; GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8; Solutions manual available upon adoptions. Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.



Introduction To Applied Optimization


Introduction To Applied Optimization
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Author : Urmila Diwekar
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-03

Introduction To Applied Optimization written by Urmila Diwekar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-03 with Mathematics categories.


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.



Introduction To Applied Optimization


Introduction To Applied Optimization
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Author : Urmila M. Diwekar
language : en
Publisher: Springer Nature
Release Date : 2020-10-29

Introduction To Applied Optimization written by Urmila M. Diwekar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-29 with Mathematics categories.


Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting; Introduces applied optimization to the hazardous waste blending problem; Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control; Includes an extensive bibliography at the end of each chapter and an index; GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8; Solutions manual available upon adoptions.



Applied Optimization


Applied Optimization
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Author : Ross Baldick
language : en
Publisher: Cambridge University Press
Release Date : 2006-05-04

Applied Optimization written by Ross Baldick and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-04 with Business & Economics categories.


Step-by-step descriptions of how to formulate numerical problems to be solved by existing software.



Introduction To Optimization


Introduction To Optimization
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Author : Pablo Pedregal
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-11-03

Introduction To Optimization written by Pablo Pedregal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-03 with Mathematics categories.


This undergraduate textbook introduces students of science and engineering to the fascinating field of optimization. It is a unique book that brings together the subfields of mathematical programming, variational calculus, and optimal control, thus giving students an overall view of all aspects of optimization in a single reference. As a primer on optimization, its main goal is to provide a succinct and accessible introduction to linear programming, nonlinear programming, numerical optimization algorithms, variational problems, dynamic programming, and optimal control. Prerequisites have been kept to a minimum, although a basic knowledge of calculus, linear algebra, and differential equations is assumed.



Applied Optimization With Matlab Programming


Applied Optimization With Matlab Programming
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Author : P. Venkataraman
language : en
Publisher: John Wiley & Sons
Release Date : 2009-03-23

Applied Optimization With Matlab Programming written by P. Venkataraman and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-23 with Technology & Engineering categories.


Technology/Engineering/Mechanical Provides all the tools needed to begin solving optimization problems using MATLAB® The Second Edition of Applied Optimization with MATLAB® Programming enables readers to harness all the features of MATLAB® to solve optimization problems using a variety of linear and nonlinear design optimization techniques. By breaking down complex mathematical concepts into simple ideas and offering plenty of easy-to-follow examples, this text is an ideal introduction to the field. Examples come from all engineering disciplines as well as science, economics, operations research, and mathematics, helping readers understand how to apply optimization techniques to solve actual problems. This Second Edition has been thoroughly revised, incorporating current optimization techniques as well as the improved MATLAB® tools. Two important new features of the text are: Introduction to the scan and zoom method, providing a simple, effective technique that works for unconstrained, constrained, and global optimization problems New chapter, Hybrid Mathematics: An Application, using examples to illustrate how optimization can develop analytical or explicit solutions to differential systems and data-fitting problems Each chapter ends with a set of problems that give readers an opportunity to put their new skills into practice. Almost all of the numerical techniques covered in the text are supported by MATLAB® code, which readers can download on the text's companion Web site www.wiley.com/go/venkat2e and use to begin solving problems on their own. This text is recommended for upper-level undergraduate and graduate students in all areas of engineering as well as other disciplines that use optimization techniques to solve design problems.



Practical Mathematical Optimization


Practical Mathematical Optimization
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Author : Jan Snyman
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-11-29

Practical Mathematical Optimization written by Jan Snyman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-29 with Mathematics categories.


This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.



Introductory Lectures On Convex Optimization


Introductory Lectures On Convex Optimization
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Author : Yurii Nesterov
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-12-31

Introductory Lectures On Convex Optimization written by Yurii Nesterov and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-31 with Mathematics categories.


It was in the middle of the 1980s, when the seminal paper by Kar markar opened a new epoch in nonlinear optimization. The importance of this paper, containing a new polynomial-time algorithm for linear op timization problems, was not only in its complexity bound. At that time, the most surprising feature of this algorithm was that the theoretical pre diction of its high efficiency was supported by excellent computational results. This unusual fact dramatically changed the style and direc tions of the research in nonlinear optimization. Thereafter it became more and more common that the new methods were provided with a complexity analysis, which was considered a better justification of their efficiency than computational experiments. In a new rapidly develop ing field, which got the name "polynomial-time interior-point methods", such a justification was obligatory. Afteralmost fifteen years of intensive research, the main results of this development started to appear in monographs [12, 14, 16, 17, 18, 19]. Approximately at that time the author was asked to prepare a new course on nonlinear optimization for graduate students. The idea was to create a course which would reflect the new developments in the field. Actually, this was a major challenge. At the time only the theory of interior-point methods for linear optimization was polished enough to be explained to students. The general theory of self-concordant functions had appeared in print only once in the form of research monograph [12].



Introduction To Optimization Methods


Introduction To Optimization Methods
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Author : P. Adby
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Introduction To Optimization Methods written by P. Adby and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Social Science categories.


During the last decade the techniques of non-linear optim ization have emerged as an important subject for study and research. The increasingly widespread application of optim ization has been stimulated by the availability of digital computers, and the necessity of using them in the investigation of large systems. This book is an introduction to non-linear methods of optimization and is suitable for undergraduate and post graduate courses in mathematics, the physical and social sciences, and engineering. The first half of the book covers the basic optimization techniques including linear search methods, steepest descent, least squares, and the Newton-Raphson method. These are described in detail, with worked numerical examples, since they form the basis from which advanced methods are derived. Since 1965 advanced methods of unconstrained and constrained optimization have been developed to utilise the computational power of the digital computer. The second half of the book describes fully important algorithms in current use such as variable metric methods for unconstrained problems and penalty function methods for constrained problems. Recent work, much of which has not yet been widely applied, is reviewed and compared with currently popular techniques under a few generic main headings. vi PREFACE Chapter I describes the optimization problem in mathemat ical form and defines the terminology used in the remainder of the book. Chapter 2 is concerned with single variable optimization. The main algorithms of both search and approximation methods are developed in detail since they are an essential part of many multi-variable methods.



Introduction To Nonlinear Optimization


Introduction To Nonlinear Optimization
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Author : Amir Beck
language : en
Publisher: SIAM
Release Date : 2014-10-27

Introduction To Nonlinear Optimization written by Amir Beck and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-27 with Mathematics categories.


This book provides the foundations of the theory of nonlinear optimization as well as some related algorithms and presents a variety of applications from diverse areas of applied sciences. The author combines three pillars of optimization?theoretical and algorithmic foundation, familiarity with various applications, and the ability to apply the theory and algorithms on actual problems?and rigorously and gradually builds the connection between theory, algorithms, applications, and implementation. Readers will find more than 170 theoretical, algorithmic, and numerical exercises that deepen and enhance the reader's understanding of the topics. The author includes offers several subjects not typically found in optimization books?for example, optimality conditions in sparsity-constrained optimization, hidden convexity, and total least squares. The book also offers a large number of applications discussed theoretically and algorithmically, such as circle fitting, Chebyshev center, the Fermat?Weber problem, denoising, clustering, total least squares, and orthogonal regression and theoretical and algorithmic topics demonstrated by the MATLAB? toolbox CVX and a package of m-files that is posted on the book?s web site.