Topics in Convex Optimisation (Michaelmas 2019)

Lecturer: Hamza Fawzi

Tue-Thu 10AM - MR13

Revision session will be on Tuesday May 12th, 3:30-5:30pm in MR9. Link to revision exercises

The following lecture notes are rough and were not proofread, so they may contain mistakes, typos, ...
  1. Lecture 1: Introduction
  2. Lecture 2: Review of convex functions
  3. Lecture 3: Gradient method
  4. Lecture 4: Lower complexity bounds
  5. Lecture 5: Fast gradient method
  6. Lecture 6: Proximal gradient method
  7. Lecture 7: Subgradient method
  8. Lecture 8: Conjugate functions
  9. Lecture 9: Smoothing
  10. Lecture 10: Lagrangian duality
  11. Lecture 11: Dual methods
  12. Lecture 12: Mirror descent
  13. Lecture 13: Newton's method
  14. Lecture 14: Self-concordant functions
  15. Lecture 15: Path-following methods
  16. Lecture 16: Linear/second-order cone/semidefinite programming

Note on subdifferential of sum of two convex functions.

Exercise sheets

  1. Exercise sheet 1. To be discussed at the first example class, Tuesday 5th November 3:30pm, MR9. Solutions
  2. Exercise sheet 2. To be discussed at the second example class, Tuesday 19th November 3:30pm, MR9. Solutions
  3. Exercise sheet 3. To be discussed at the third example class, Tuesday 3rd December 3:30pm, MR9. Solutions

References