Quick links: labs, discussion board, upload system, timetable.
This course is about optimisation in finite-dimensional (Euclidean) spaces. It includes least squares problems, linear programming and convex optimisation. You will learn:
Lecturer: Tomáš Werner
Schedule (may change during the term):
| # | date | topic | optional materials |
|---|---|---|---|
| 01 | 02.10. | Matrix algebra. | |
| 02 | 09.10. | Recap of parts of linear algebra. | EE263 lect. 2-4 |
| 03 | 16.10. | Ortogonality, QR decomposition. | EE263 lect. 4-5 |
| 04 | 23.10. | Least squares, least norm. | EE263 lect. 5-6,9 |
| 05 | 30.10. | Quadratic functions, spectral decomposition. | EE263 lect. 15-17 |
| 06 | 06.11. | Quadratic functions, spectral decomposition. | |
| 07 | 13.11. | SVD | |
| 08 | 20.11. | Multivariate calculus. | |
| 09 | 27.11. | Local extrema, free and equality-constrained. | Luenberger 7.1-7.3, 11.1-11.4 |
| 10 | 04.12. | Numerical algorithms to find free local extrema. | Luenberger 8.8 |
| 11 | 11.12. | Linear programming. Convex sets, convex polyhedra. | Luenberger 2.1-2.6 |
| 12 | 18.12. | Simplex method. | Luenberger 3.1-3.5 |
| 13 | 08.01. | Convex functions, convex optimization. | EE364a lect. 3-4 |
| 14 | 15.01. | Duality. | EE364a lect. 5 |
Here are lecture notes (partially translated into English, the translation will gradually progress).
“Luenberger” refers to the book David Luenberger - Yinyu Ye: Linear and Nonlinear Programming.
Here is optional literature.
The total number of points is the sum of:
Necessary condition for passing the course is passing the labs and min. 25 points from the exam. The final mark is then determined by the table:
| points | [0,50) | [50,60) | [60,70) | [70,80) | [80,90) | [90,100] |
|---|---|---|---|---|---|---|
| mark | F | E | D | C | B | A |