| # | Date | Topic |
Linear Programming |
| 1 |
1/11 |
Introduction, simplex method
|
| 2 |
1/16 |
Duality, Ellipsoid method
|
| 3 |
01/18 |
Interior point method, Megiddo's algorithm
|
| 4 |
01/23 |
Randomized algorithms for LP's
|
Parametric Searching |
| 5 |
01/25 |
Basic algorithm
|
| 6 |
01/30 |
Extensions and variants
See above. |
| - |
02/01 |
No class - Snow Day |
| 7 |
02/06 |
Multi-dimensional Parametric Searching
|
| 8 |
02/08 |
Randomized techniques
|
ε-approximation and ε-nets |
| 9 |
02/13 |
Basic definitions, applications
|
| 10 |
02/15 |
Deterministic algorithms, discrepancy, cuttings
|
Core Sets |
| 11 |
02/19 |
Approximating directional width, extent measures
|
| 12 |
02/20 |
Coresets for polynomials, kinetic geometry
◊ See above. |
Shape Fitting and Clustering |
| 13 |
02/22 |
1-center, 1-median, SVD
|
| 14 |
02/27 |
k-center, k-median
|
| 15 |
03/01 |
Spectral clustering, projective clustering
|
Geometric Packing and Covering |
| 16 |
03/06 |
Greedy and randomized algorithms
|
| 17 |
03/08 |
Art gallery problems
|
Network Design |
| 18 |
03/20 |
Spanners
|
| 19 |
03/22 |
Well-separated pair decomposition
|
| 20 |
03/27 |
Minimum-weight matching
|
| 21 |
03/29 |
Arora's TSP algorithm
|
| 22 |
04/03 |
Arora's TSP algorithm (cont.)
See above. |
Shape Matching |
| 23 |
04/05 |
Hausdorff and Frechet distance
|
| 24 |
04/10 |
ICP and EMD
(Guest Lecture: Jeff Phillips)
|
Embeddings |
| 25 |
04/12 |
Johnson Lindenstrauus Lemma
(Guest Lecture: Hai Yu)
|
| 26 |
04/17 |
Embeddings into trees, Euclidean spaces
(Guest Lecture: Hai Yu)
◊ No Lecture Notes |
