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Cornell

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.

Textbook: Cornell computational optimization open textbook.