Engineering Design Optimization
eBook - PDF

Engineering Design Optimization

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

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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Yes, you can access Engineering Design Optimization by Joaquim R. R. A. Martins,Andrew Ning in PDF and/or ePUB format, as well as other popular books in Mathematics & Optimization. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half-title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Preface
  7. Acknowledgements
  8. 1 Introduction
  9. 2 A Short History of Optimization
  10. 3 Numerical Models and Solvers
  11. 4 Unconstrained Gradient-Based Optimization
  12. 5 Constrained Gradient-Based Optimization
  13. 6 Computing Derivatives
  14. 7 Gradient-Free Optimization
  15. 8 Discrete Optimization
  16. 9 Multiobjective Optimization
  17. 10 Surrogate-Based Optimization
  18. 11 Convex Optimization
  19. 12 Optimization Under Uncertainty
  20. 13 Multidisciplinary Design Optimization
  21. A Mathematics Background
  22. B Linear Solvers
  23. C Quasi-Newton Methods
  24. D Test Problems
  25. Bibliography
  26. Index