Convex Optimization (NMMB409) - Winter term 2026/27
Lecture: Monday 12:20 - 13:50, K2. Wednesday 10:40 - 12:10, K8.
Practicals: Wednesday 12:20 - 15:50, K8., run by
Max Hadek
Literature:
- [BV] Boyd, Vandenberghe, Convex Optimization
This is the main source for the course. Book and slides are freely available on Boyd's website.
- [MG] Matoušek, Gärtner, Understanding and Using Linear Programming
- Any programming examples will be done using the Python package CVXPY (CVXPY).
Evaluation:
Zápočet: To obtain the credit (zápočet) one needs to be positive on 2 out of 3 tests in the exercise classes.
Exam: oral examination. It is necessary to have obtained the credit (zápočet) in order to take the exam.
Consulation: If you have any questions, do not hesitate to ask! Longer discussions best in my office hour, Mo: 14:00-15:30.
Overview:
| Date |
Topics |
Reference |
Exercises |
28.9 |
no lecture! (St. Wenceslas Day) |
|
|
| 30.9 |
Examples, historic overview; linear (and convex) programs can be solved efficiently
Pr: Basic examples in CVXPY |
[MG2],[BV1] |
Ex1, Code |
| 5.10 |
LP relaxations (Job Assignment Example). Def: half-spaces, convex polyhedra and their vertices; LPs in equational normal form |
[MG3.2, 4] |
|
| 7.10 |
|
|
Ex2 |
| 12.10 |
|
|
|
| 14.10 |
|
|
Ex3 |
| 19.10 |
|
|
|
| 21.10 |
|
|
Ex4 |
| 26.10 |
|
|
|
28.10 |
no lecture/practical! (Independent Czechoslovak State Day) |
|
|
| 2.11 |
|
|
|
| 4.11 |
|
|
Ex5 |
| 9.11 |
|
|
|
| 11.11 |
|
|
Ex6 |
| 16.11 |
|
|
|
| 18.11 |
|
|
Ex7 |
| 23.11 |
|
|
|
| 25.11 |
|
|
Ex8 |
| 30.11 |
|
|
|
| 2.12 |
|
|
Ex9 |
| 7.12 |
|
|
|
| 9.12 |
|
|
Ex10 |
| 14.12 |
|
|
|
| 16.12 |
|
|
Ex11 |
| 4.1 |
|
|
|
| 6.1 |
|
|
Ex12 |