This is an online student-contributed open-source text covering a variety of topics on process optimization.
The goal of the project is to provide the greater scientific and engineering community with a useful and relevant resource on computational optimization methods and applications.
Sections:
- Linear Programming (LP).
- NonLinear Programming (NLP).
- Deterministic Global Optimization.
- Dynamic Programming.
- Traditional Applications.
- Emerging Applications.
- Mixed-Integer Linear Programming (MILP).
- Mixed-Integer NonLinear Programming (MINLP).
- Optimization under Uncertainty.
- Optimization for Machine Learning and Data Analytics.
- Black-box Optimization.