Practical Mathematical Optimization

Guides readers to understand processes and strategies in real world optimization problems Contains new material on gradient-based methods, algorithm implementation via Python, and basic optimization principles Covers fundamental optimization concepts and definitions, search techniques for unconstrained minimization and standard methods for constrained optimization Includes example problems and exercises

Autorentext

Jan A. Snyman currently holds the position of emeritus professor in the Department of Mechanical and Aeronautical Engineering of the University of Pretoria, having retired as full professor in 2005. He has taught physics, mathematics and engineering mechanics to science and engineering students at undergraduate and postgraduate level, and has supervised the theses of 26 Masters and 8 PhD students. His research mainly concerns the development of gradient-based trajectory optimization algorithms for solving noisy and multi-modal problems, and their application in approximation methodologies for the optimal design of engineering systems. He has authored or co-authored 89 research articles in peer-reviewed journals as well as numerous papers in international conference proceedings.

Daniel N. Wilke is a senior lecturer in the Department of Mechanical and Aeronautical Engineering of the University of Pretoria. He teaches computer programming, mathematicalprogramming and computational mechanics to science and engineering students at undergraduate and postgraduate level. His current research focuses on the development of interactive design optimization technologies, and enabling statistical learning (artificial intelligence) application layers, for industrial processes and engineering design. He has co-authored over 50 peer-reviewed journal articles and full length conference papers.

Zusammenfassung

Inhalt

1.Introduction.- 2.Line search descent methods for unconstrained minimization.-3. Standard methods for constrained optimization.-4. Basic Example Problems.- 5. Some Basic Optimization Theorems.- 6. New gradient-based trajectory and approximation methods.- 7. Surrogate Models.- 8. Gradient-only solution strategies.- 9. Practical computational optimization using Python.- Appendix.- Index.