
- English
- PDF
- Available on iOS & Android
Engineering Design Optimization
About this book
Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.
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Information
Table of contents
- Cover
- Half-title Page
- Title Page
- Copyright Page
- Contents
- Preface
- Acknowledgements
- 1 Introduction
- 2 A Short History of Optimization
- 3 Numerical Models and Solvers
- 4 Unconstrained Gradient-Based Optimization
- 5 Constrained Gradient-Based Optimization
- 6 Computing Derivatives
- 7 Gradient-Free Optimization
- 8 Discrete Optimization
- 9 Multiobjective Optimization
- 10 Surrogate-Based Optimization
- 11 Convex Optimization
- 12 Optimization Under Uncertainty
- 13 Multidisciplinary Design Optimization
- A Mathematics Background
- B Linear Solvers
- C Quasi-Newton Methods
- D Test Problems
- Bibliography
- Index