EECS 127/227AT

Optimization Models in Engineering

Notes taken in Professor Gireeja Ranade's Fall 2022 EECS 127 course. Please note that these are my unmodified class notes, so they may have errors or notes to myself.

CS 189*  /  CS 188*  /  EE 120  /  EECS 151*  /  EECS 127  /  EECS 126  /  EECS 16B  /  EECS 16A*  /  MATH H53

1. No Notes. Introduction
2. Linear Algebra Review Vector Norms, Gram-Schmidt and QR, Fundamental Theorem of Linear Algebra
3. Linear Algebra Review Symmetric Matrices
4. Principal Component Analysis
5. SVD and Low-Rank Approximation
6. Low-Rank Approximation
7. Vector Calculus
8. Ridge Regression
9. Connections: Ridge, PCA, MLE
10. Convexity 1
11. Convexity 2
12. Descent Methods
13. Descent Methods & Convex Optimization
14. No Notes. Bonus Lecture: Principal Components Regression, Total Least Squares
15. Weak Duality
16. Strong Duality
17. KKT, Optimality Conditions
18. KKT, Formulating Optimization Problems
19. Linear Programs
20. QPs & SOCPs
21. SOCPs & Newtons Method
22. L1 & LASSO
23. Advanced Descent Methods
24. Applications: LQR Control
25. Applications: SVMs
26. SVM Applications

• Course Summary

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