This course is an abbreviated version of EE364A to be offered in the summer only. This course cannot be counted towards depth or breadth, but can be counted as other EE units.
Part 1: Theory (2.5-3 weeks: 5 lectures)
1. Convex sets
2. Convex functions
3. Convex optimization problems
4. Duality
Part 2: Applications (3 weeks: 6 lectures)
5. Approximation and fitting
6. Statistical estimation
7. Geometric problems
Part 3: Algorithms (1.5-2 weeks: 3 lectures)
8. Numerical linear algebra
9. Unconstrained minimization
10. Equality constrained minimization
11. Interior point methods
we'll cover almost all the topics, but some topics we'll cover in much less detail (i.e. fewer examples, no proofs). these are:
- quasiconvex functions and quasiconvex optimization problems
- perturbation and sensitivity analysis
- theorems of alternatives
- duality for problems with generalized inequalities
- proofs of convergence for Newton's method
- self-concordance
- numerical linear algebra