|
|
|
Lecture notes/slides will be posted here after class
(Beware: Some lecture slides will not be available online. I will note that in class before I teach
so that you can take notes if you want).
Lecture 1: Introduction
- Sariel's Geometric Approximation Algorithms Book: Section 1.2
- David Mount's Notes on NN on grids
Lecture 2: D&C
Lecture 3: Stable Marriage [demo]
Lecture 4: Max Flow [Demo]
Lecture 5: Applications of Network Flows
Lecture 6: PageRank
Lecture 7: Reductions and NP
Lecture 8: NP-Completeness
Lecture 9: Approximation and Hardness of Approximation
Lecture 10: Linear Programming
Lecture 11: Backwards Analysis and LP
- Relation to MEBs
- (1+eps) approximation of MEBs
- k-center approximation algorithms
Lecture 12: Approximations using LP : Luca's notes
Lecture 13: DP and PTAS using DP
Lecture 14: Randomized Algorithms
Lecture 15: D&C: PRAM Introduction
Lecture 16: Parallel Algorithms
Lecture 17: String Algorithms: Suffix Arrays.
Lecture 18: Machine Learning: Perceptrons, SVMs.
Lecture 19: Computational Geometry: Convex Hulls (Till Lecture 4).
Lecture 20: Online Algorithms
|