Description

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

Textbooks

Terms Offered

  • 2020 Summer

Materials

Syllabus

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

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