University of Jyväskylä Lecturer Markus Hartikainen, PhD
These are the materials from Nonlinear Optimization course taught at the University of Jyväskylä, Finland Jan-March 2015.
Introduction
1. First hour: Basic information about the course and study methods (Python, Jupyter etc.); second hour: A very fast introduction to Python and Jupyter using Robert Johansson's Scienfic Python Lectures (https://github.com/jrjohansson/scientific-python-lectures).
2. The very basics: what is an optimization problem, how to solve one, and line search
Unconstrained optimization
3. Direct search methods: Coordinate descent, Hooke&Jeeves, and Powell's methods
4. Steepest Descent and Newton's method for unrestricted optimization
5. Example of using available software: scipy.optimize
Constrained optimization
6. Optimality conditions
7. Indirect methods for constrained optimization
8. Direct methods for constrained optimization (part 1)
9. Direct methods for constrained optimization (part 2)
Multiobjective optimization
10. What is multiobjective optimization
11. How to solve multiobjective optimization problems
Applications of optimization
12. Applications of optimization (part 1)
13. Applications of optimization (part 2)
Optimization software
14. Algebraic modelling languages, especially Pyomo
15. IND-NIMBUS(R)
Wrapping up
16. How to find and read scientific papers in the field
17. Further topics and current research in optimization
18. Review
This course is taught using Jupyter notebooks (http://ipython.org/notebook.html, formerly known as IPython notebooks).
The source code of the Jupyter notebooks thought in this course will be available at Github at https://github.com/maeehart/TIES483.
The Jupyter notebooks will be transferred to slides using https://github.com/damianavila/RISE. To view the slides at home, you do not need to have this installed.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.