| Summary, etc |
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.<br/><br/>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.<br/><br/>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.<br/><br/>Providing excellent reference for students or professionals, Engineering Optimization:<br/><br/> 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<br/> Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values<br/> Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling<br/> Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book<br/><br/>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. |
