國立虎尾科技大學 |

Engineering optimization : = applications, methods and analysis /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Engineering optimization :/ R. Russell Rhinehart.
其他題名:	applications, methods and analysis /
作者:	Rhinehart, R. Russell,
面頁冊數:	1 online resource (770 pages) :illustrations. :
標題:	Engineering - Mathematical models. -
電子資源:	http://ebooks.asmedigitalcollection.asme.org/book.aspx?bookid=2426
ISBN:	9781118936320 (e-ISBN)

Engineering optimization : = applications, methods and analysis /
Rhinehart, R. Russell,1946-

Engineering optimization :applications, methods and analysis /R. Russell Rhinehart. - 1 online resource (770 pages) :illustrations.

Includes bibliographical references and index.

Front Matter -- 1 Optimization: Introduction and Concepts -- 2 Optimization Application Diversity and Complexity -- 3 Validation: Knowing That the Answer Is Right -- 4 Univariate (Single DV) Search Techniques -- 5 Path Analysis -- 6 Stopping and Convergence Criteria: 1-D Applications -- 7 Multidimension Application Introduction and the Gradient -- 8 Elementary Gradient-Based Optimizers: CSLS and ISD -- 9 Second-Order Model-Based Optimizers: SQ and NR -- 10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG -- 11 Direct Search Techniques -- 12 Linear Programming -- 13 Dynamic Programming -- 14 Genetic Algorithms and Evolutionary Computation -- 15 Intuitive Optimization -- 16 Surface Analysis II -- 17 Convergence Criteria 2: N-D Applications -- 18 Enhancements to Optimizers -- 19 Scaled Variables and Dimensional Consistency -- 20 Economic Optimization -- 21 Multiple OF and Constraint Applications -- 22 Constraints -- 23 Multiple Optima -- 24 Stochastic Objective Functions -- 25 Effects of Uncertainty -- 26 Optimization of Probable Outcomes and Distribution Characteristics -- 27 Discrete and Integer Variables -- 28 Class Variables -- 29 Regression -- 30 Perspective -- 31 Response Surface Aberrations -- 32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints -- 33 Evaluating Optimizers -- 34 Troubleshooting Optimizers -- 35 Analysis of Leapfrogging -- 36 Case Study 1: Economic Optimization of a Pipe System -- 37 Case Study 2: Queuing Study -- 38 Case Study 3: Retirement Study -- 39 Case Study 4: A Goddard Rocket Study -- 40 Case Study 5: Reservoir -- 41 Case Study 6: Area Coverage -- 42 Case Study 7: Approximating Series Solution to an ODE -- 43 Case Study 8: Horizontal Tank Vapor–Liquid Separator -- 44 Case Study 9: In Vitro Fertilization -- 45 Case Study 10: Data Reconciliation -- Back Matter.

Restricted to subscribers or individual electronic text purchasers.

Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.

Electronic reproduction.
New York, N.Y. :
The American Society of Mechanical Engineers,
2018.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

In English.

ISBN: 9781118936320 (e-ISBN)

Standard No.: 10.1115/1.861OPT doi

Publisher. No.: 861OPTasme

ASME, Two Park Avenue. New York, NY 10016

LCCN: 2017058922Subjects--Topical Terms:

591361
Engineering
--Mathematical models.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TA342

Dewey Class. No.: 620.001/5196

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505 0 $a Front Matter -- 1 Optimization: Introduction and Concepts -- 2 Optimization Application Diversity and Complexity -- 3 Validation: Knowing That the Answer Is Right -- 4 Univariate (Single DV) Search Techniques -- 5 Path Analysis -- 6 Stopping and Convergence Criteria: 1-D Applications -- 7 Multidimension Application Introduction and the Gradient -- 8 Elementary Gradient-Based Optimizers: CSLS and ISD -- 9 Second-Order Model-Based Optimizers: SQ and NR -- 10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG -- 11 Direct Search Techniques -- 12 Linear Programming -- 13 Dynamic Programming -- 14 Genetic Algorithms and Evolutionary Computation -- 15 Intuitive Optimization -- 16 Surface Analysis II -- 17 Convergence Criteria 2: N-D Applications -- 18 Enhancements to Optimizers -- 19 Scaled Variables and Dimensional Consistency -- 20 Economic Optimization -- 21 Multiple OF and Constraint Applications -- 22 Constraints -- 23 Multiple Optima -- 24 Stochastic Objective Functions -- 25 Effects of Uncertainty -- 26 Optimization of Probable Outcomes and Distribution Characteristics -- 27 Discrete and Integer Variables -- 28 Class Variables -- 29 Regression -- 30 Perspective -- 31 Response Surface Aberrations -- 32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints -- 33 Evaluating Optimizers -- 34 Troubleshooting Optimizers -- 35 Analysis of Leapfrogging -- 36 Case Study 1: Economic Optimization of a Pipe System -- 37 Case Study 2: Queuing Study -- 38 Case Study 3: Retirement Study -- 39 Case Study 4: A Goddard Rocket Study -- 40 Case Study 5: Reservoir -- 41 Case Study 6: Area Coverage -- 42 Case Study 7: Approximating Series Solution to an ODE -- 43 Case Study 8: Horizontal Tank Vapor–Liquid Separator -- 44 Case Study 9: In Vitro Fertilization -- 45 Case Study 10: Data Reconciliation -- Back Matter.
506 $a Restricted to subscribers or individual electronic text purchasers.
506 $a Subscription required for access to full text.
506 $a License restrictions may limit access.
520 3 $a Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.
530 $a Also available in print and PDF edition.
530 $a Full text article also available for purchase.
533 $a Electronic reproduction. $b New York, N.Y. : $c The American Society of Mechanical Engineers, $d 2018. $n Mode of access: World Wide Web. $n System requirements: Web browser. $n Access may be restricted to subscribers or individual electronic text purchasers.
538 $a System requirements: Adobe Acrobat Reader.
538 $a Mode of access: Internet via World Wide Web.
546 $a In English.
588 $a Title from PDF title page (ASME eBooks Web site, viewed on August 29, 2018).
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650 0 $a Mathematical optimization. $3 527675
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655 0 $a Electronic books. $2 local $3 554714
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