# | 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 |