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Présentation Engineering Optimization de R. Russell Rhinehart Format Relié
- Livre
Résumé : An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus...
Biographie: Preface xix? Acknowledgments xxvii? Nomenclature xxix? About the Companion Website xxxvii? Section 1 Introductory Concepts 1? 1 Optimization: Introduction and Concepts 3? 2 Optimization Application Diversity and Complexity 33? 3 Validation: Knowing That the Answer Is Right 53? Section 2 Univariate Search Techniques 59? 4 Univariate (Single DV) Search Techniques 61? 5 Path Analysis 93? 6 Stopping and Convergence Criteria: 1-D Applications 107? Section 3 Multivariate Search Techniques 117? 7 Multidimension Application Introduction and the Gradient 119? 8 Elementary Gradient-Based Optimizers: CSLSandISD135? 9 Second-Order Model-Based Optimizers:SQandNR155? 10 Gradient-Based Optimizer Solutions:LM, RLM, CG, BFGS, RG, and GRG173? 11 Direct Search Techniques 187? 12 Linear Programming 223? 13 Dynamic Programming 233? 14 Genetic Algorithms and Evolutionary Computation 243? 15 Intuitive Optimization 253? 16 Surface Analysis II 257? 17 Convergence Criteria 2: N-D Applications 265? 18 Enhancements to Optimizers 271? Section 4 Developing Your Application Statements 279? 19 Scaled Variables and Dimensional Consistency 281? 20 Economic Optimization 289? 21 Multiple OF and Constraint Applications 305? 22 Constraints 319? 23 Multiple Optima 335? 24 Stochastic Objective Functions 353? 25 Effects of Uncertainty 367? 26 Optimization of Probable Outcomes and Distribution Characteristics 381? 27 Discrete and Integer Variables 391? 28 Class Variables 397? 29 Regression 403? Section 5 Perspective on Many Topics 441? 30 Perspective 443? 31 Response Surface Aberrations 459? 32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475? 33 Evaluating Optimizers 489? 34 Troubleshooting Optimizers 499? Section 6 Analysis of Leapfrogging Optimization 505? 35 Analysis of Leapfrogging 507? Section 7 Case Studies 529? 36 Case Study 1: Economic Optimization of a Pipe System 531? 37 Case Study 2: Queuing Study 539? 38 Case Study 3: Retirement Study 543? 39 Case Study 4: AGoddard Rocket Study 551? 40 Case Study 5: Reservoir 557? 41 Case Study 6: Area Coverage 561? 42 Case Study 7: Approximating Series Solution to an ODE 565? 43 Case Study 8: Horizontal Tank Vapor-Liquid Separator 571? 44 Case Study 9: In Vitro Fertilization 579? 45 Case Study 10: Data Reconciliation 585? Section 8 Appendices 591? Section 9 References and Index 717? References and Additional Resources 719? Index 723
Sommaire:
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.
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