Lectures

Hand-written lecture notes:

Date	Notes	Table of contents
20.10.	Lecture 1	Chapter I: Introduction
22.10.	Lecture 2	Chapter II: Minimizing a convex function
27.10.	Lecture 3	Chapter III: Conic Optimization §1 Convex cones
29.10	Lecture 4	§2 The PSD cone
3.11	Lecture 5	§3 Conic programs
5.11	Lecture 6	§4 Theorem of alternatives
10.11	Lecture 7
12.11	Lecture 8	§5 Duality theory
17.11	Lecture 9	Chapter IV: First applications §1 Robust optimization
19.11	Lecture 10	§2 Eigenvalue optimization
24.11	Lecture 11	§3 SDP relaxations of quadratic programs Chapter V: Approximation algorithms §1 MAX CUT
26.11	Lecture 12
1.12	Lecture 13
3.12	Lecture 14	§2 Eigenvalue interpretation of the SDP relaxation for MAX CUT §3 Little Grothendieck inequality / Nesterov's approximation algorithm
8.12	Lecture 15	§4 Grothendieck's inequality
10.12	Lecture 16	Chapter VI: Determinant Maximization §1 Convex spectral functions
15.12	Lecture 17	§2 MAXDET optimization
17.12	Lecture 18
22.12	Lecture 19	§3 Approximation of polytopes by ellipsoids
7.1	Lecture 20
12.1	Lecture 21
14.1	Lecture 22	Chapter VII: Packings and colorings in graphs §1 Basic definitions §2 Semidefinite relexation for $\alpha$ and $\chi$
19.1	Lecture 23	§3 Perfect Graphs
21.1	Lecture 24
26.1	Lecture 25	§4 Shannon capacity
28.1	Lecture 26
2.2	Lecture 27	Chapter VIII: Copositive Programming §1 The completely positive and the copositive cone
4.2	Lecture 28	§2 A copositive reformulation of the independence number of a graph
9.2	Lecture 29	Chapter IX: Polynomial Optimization §1 Nonnegative polynomials and sum of squares
11.2 (fällt aus)	Lecture 30	§2 Global optimization with polynomials