Numerical Optimization

Description

Various optmization methods used in scientific computing through the Julia language. 10-week course, hosted through WebEx, and open to anyone interested.

Textbooks

• Algorithms for Optimization by Mykel J. Kochenderfer and Tim A. Wheeler

Terms Offered

• 2020 Summer

Materials

Syllabus

• README.md

GitHub

All of the Julia lecture notebooks and source TeX files can be found on the course GitHub page.

Lectures

1. Introduction
2. Derivative
3. Bracket
4. Local Descent
5. First order 1
6. First order 2
7. Newton’s methods
8. Quasi-Newton methods
9. Direct methods
10. Stochastic methods
11. Evolutionary methods
12. Constrained optimization
13. Sampling plans
14. Surrogate model
15. Gaussian Process Regression
16. Surrogate optimization
17. Uncertainty
18. Symbolic Regression