Topics in Convex Optimisation (Michaelmas 2019)
Lecturer: Hamza Fawzi
Tue-Thu 10AM - MR13
The following lecture notes are rough and were not proofread, so they may contain mistakes, typos, ...
- Lecture 1: Introduction
- Lecture 2: Review of convex functions
- Lecture 3: Gradient method
- Lecture 4: Lower complexity bounds
- Lecture 5: Fast gradient method
- Lecture 6: Proximal gradient method
- Lecture 7: Subgradient method
- Lecture 8: Conjugate functions
- Lecture 9: Smoothing
- Lecture 10: Lagrangian duality
- Lecture 11: Dual methods
- Lecture 12: Mirror descent
- Lecture 13: Newton's method
- Lecture 14: Self-concordant functions
- Lecture 15: Path-following methods
- Lecture 16: Linear/second-order cone/semidefinite programming
Exercise sheets
- Exercise sheet 1
- Exercise sheet 2
- Exercise sheet 3
References