{"product_id":"engineering-optimization-applications-methods-and-analysis-hardback-9781118936337","title":"Engineering Optimization; Applications, Methods and Analysis (Hardback) 9781118936337","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eEngineering Optimization\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eApplications, Methods and Analysis\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eR. Russell Rhinehart (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118936337, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 11 April 2018\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e784 pages\u003cbr\u003e23.9 x 19.8 x 4.8 cm, 1.61 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn Application-Oriented Introduction to Essential Optimization Concepts and Best Practices\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOptimization 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. \u003ci\u003eEngineering Optimization\u003c\/i\u003e provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process.\u003c\/p\u003e \u003cp\u003eAlthough 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.\u003c\/p\u003e \u003cp\u003eExamples, 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.\u003c\/p\u003e \u003cp\u003eProviding excellent reference for students or professionals, \u003ci\u003eEngineering Optimization\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDescribes 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\u003c\/li\u003e \u003cli\u003eProvides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values\u003c\/li\u003e \u003cli\u003eDetails current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling\u003c\/li\u003e \u003cli\u003eProvides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eClear 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix \u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii \u003c\/p\u003e \u003cp\u003eNomenclature xxix \u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxxvii \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 1 Introductory Concepts 1\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e1 Optimization: Introduction and Concepts 3 \u003c\/p\u003e \u003cp\u003e2 Optimization Application Diversity and Complexity 33 \u003c\/p\u003e \u003cp\u003e3 Validation: Knowing That the Answer Is Right 53 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 2 Univariate Search Techniques 59\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e4 Univariate (Single DV) Search Techniques 61 \u003c\/p\u003e \u003cp\u003e5 Path Analysis 93 \u003c\/p\u003e \u003cp\u003e6 Stopping and Convergence Criteria: 1-D Applications 107 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 3 Multivariate Search Techniques 117\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e7 Multidimension Application Introduction and the Gradient 119 \u003c\/p\u003e \u003cp\u003e8 Elementary Gradient-Based Optimizers: \u003ci\u003eCSLS\u003c\/i\u003eand\u003ci\u003eISD\u003c\/i\u003e135 \u003c\/p\u003e \u003cp\u003e9 Second-Order Model-Based Optimizers:\u003ci\u003eSQ\u003c\/i\u003eand\u003ci\u003eNR\u003c\/i\u003e155 \u003c\/p\u003e \u003cp\u003e10 Gradient-Based Optimizer Solutions:\u003ci\u003eLM\u003c\/i\u003e, \u003ci\u003eRLM\u003c\/i\u003e, \u003ci\u003eCG\u003c\/i\u003e, \u003ci\u003eBFGS\u003c\/i\u003e, \u003ci\u003eRG\u003c\/i\u003e, and \u003ci\u003eGRG\u003c\/i\u003e173 \u003c\/p\u003e \u003cp\u003e11 Direct Search Techniques 187 \u003c\/p\u003e \u003cp\u003e12 Linear Programming 223 \u003c\/p\u003e \u003cp\u003e13 Dynamic Programming 233 \u003c\/p\u003e \u003cp\u003e14 Genetic Algorithms and Evolutionary Computation 243 \u003c\/p\u003e \u003cp\u003e15 Intuitive Optimization 253 \u003c\/p\u003e \u003cp\u003e16 Surface Analysis II 257 \u003c\/p\u003e \u003cp\u003e17 Convergence Criteria 2: N-D Applications 265 \u003c\/p\u003e \u003cp\u003e18 Enhancements to Optimizers 271 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 4 Developing Your Application Statements 279\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e19 Scaled Variables and Dimensional Consistency 281 \u003c\/p\u003e \u003cp\u003e20 Economic Optimization 289 \u003c\/p\u003e \u003cp\u003e21 Multiple OF and Constraint Applications 305 \u003c\/p\u003e \u003cp\u003e22 Constraints 319 \u003c\/p\u003e \u003cp\u003e23 Multiple Optima 335 \u003c\/p\u003e \u003cp\u003e24 Stochastic Objective Functions 353 \u003c\/p\u003e \u003cp\u003e25 Effects of Uncertainty 367 \u003c\/p\u003e \u003cp\u003e26 Optimization of Probable Outcomes and Distribution Characteristics 381 \u003c\/p\u003e \u003cp\u003e27 Discrete and Integer Variables 391 \u003c\/p\u003e \u003cp\u003e28 Class Variables 397 \u003c\/p\u003e \u003cp\u003e29 Regression 403 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 5 Perspective on Many Topics 441\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e30 Perspective 443 \u003c\/p\u003e \u003cp\u003e31 Response Surface Aberrations 459 \u003c\/p\u003e \u003cp\u003e32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475 \u003c\/p\u003e \u003cp\u003e33 Evaluating Optimizers 489 \u003c\/p\u003e \u003cp\u003e34 Troubleshooting Optimizers 499 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 6 Analysis of Leapfrogging Optimization 505\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e35 Analysis of Leapfrogging 507 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 7 Case Studies 529\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e36 Case Study 1: Economic Optimization of a Pipe System 531 \u003c\/p\u003e \u003cp\u003e37 Case Study 2: Queuing Study 539 \u003c\/p\u003e \u003cp\u003e38 Case Study 3: Retirement Study 543 \u003c\/p\u003e \u003cp\u003e39 Case Study 4: A\u003ci\u003eGoddard\u003c\/i\u003e Rocket Study 551 \u003c\/p\u003e \u003cp\u003e40 Case Study 5: Reservoir 557 \u003c\/p\u003e \u003cp\u003e41 Case Study 6: Area Coverage 561 \u003c\/p\u003e \u003cp\u003e42 Case Study 7: Approximating Series Solution to an ODE 565 \u003c\/p\u003e \u003cp\u003e43 Case Study 8: Horizontal Tank Vapor–Liquid Separator 571 \u003c\/p\u003e \u003cp\u003e44 Case Study 9: In Vitro Fertilization 579 \u003c\/p\u003e \u003cp\u003e45 Case Study 10: Data Reconciliation 585 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 8 Appendices 591\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 9 References and Index 717\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eReferences and Additional Resources 719 \u003c\/p\u003e \u003cp\u003eIndex 723\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ASME Press Series","offers":[{"title":"Brand New","offer_id":52512134725912,"sku":"9781118936337","price":100.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781118936337.jpg?v=1786614145","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/engineering-optimization-applications-methods-and-analysis-hardback-9781118936337","provider":"Freshly Printed Books","version":"1.0","type":"link"}