This is a graduate-level textbook covering a range of fundamental to advanced optimization theory and algorithms with practical tips, numerous illustrations, and engineering examples.
Table of contents:
- Introduction.
- A short history of optimization.
- Numerical models and solvers.
- Unconstrained gradient-based optimization.
- Constrained gradient-based optimization.
- Computing derivatives.
- Gradient-free optimization.
- Discrete optimization.
- Multiobjective optimization.
- Surrogate-based optimization.
- Convex optimization.
- Optimization under uncertainty.
- Multidisciplinary design optimization.
Textbook: Engineering design optimization.
