Computational and Metric Geometry
Winter Quarter 2017
Instructor: Yury Makarychev
Course: TTIC 31100 and CMSC 390101 Lectures: Monday & Wednesday, 1:30–2:50 pm, TTIC, room 530 Mailing list: geometryclass2017@ttic.edu . To subscribe to the course mailing list, send an email to geometryclass2017+subscribe@ttic.edu (with any subject and body). You should get a confirmation email in a few minutes; reply to this email or click on a link inside. If you cannot subscribe to the mailing list, please contact me. Textbook: Computational Geometry by M. de Berg, O. Cheong, M. van Kreveld, M. Overmars. Requirements: There will be 3 or 4 homework assignments.
Description: The course covers fundamental concepts, algorithms and techniques in computational and metric geometry. Topics covered include: convex hulls, polygon triangulations, range searching, segment intersection, Voronoi diagrams, Delaunay triangulations, metric and normed spaces, lowdistortion metric embeddings and their applications in approximation algorithms, padded decomposition of metric spaces, Johnson–Lindenstrauss transform and dimension reduction, approximate nearest neighbor search and localitysensitive hashing.

W. Kandinsky: Mild Tension, 1923 
Homework
 Problem Set 1 (due on Monday, February 6)
 Problem Set 2 (due on Wednesday, February 22)
 Problem Set 3 (due on Wednesday, March 8)
Lecture Notes and References
 Basic Properties of Metric and Normed Spaces
 Bourgain's Theorem
 Sparsest Cut Problem
 Partitioning Metric Spaces
 Survey on Geometry, Flows Graph Partitioning Algorithms by S. Arora, S. Rao and U. Vazirani (Communications of ACM, Oct 2008)
 Dimension Reduction
Tentative Schedule
 January 4: Convexity
convex sets, convex hulls, vertices, supporting lines, edges, different definitions and basic properties, Caratheodory's theorem  January 9: Convex Hulls and Line Segment Intersections
Jarvis March, Andrew's algorithm (Chapter 1.2), sweep line algorithms, line segment intersection, Bentley–Ottmann algorithm (Chapter 2.1)  January 11: Planar Graphs and Overlays
graphs, graph drawings, plane and planar graphs, Euler's formula, data structure for plane graphs, computing overlays (Chapter 2)  January 16: Martin Luther King Jr. Day. TTIC is closed.
 January 18: SODA 2017. No lecture.
 January 23: Orthogonal Range Searching
binary search, kdtrees, range trees (Chapter 5)  January 25: Point Location
trapezoidal maps, randomized algorithm (Chapter 6)  January 30: Voronoi Diagrams
Voronoi diagrams, Fortune's algorithm (Chapter 7)  February 1: Delaunay Triangulations I
triangulations, Delaunay and locally Delaunay triangulations: definitions, existence and equivalence (Chapter 9)  February 6: Delaunay Triangulations II. Metric Spaces.
duality between Delaunay triangulations and Voronoi diagrams, angle optimality (Chapter 9); metric and normed spaces–basic definitions (see lecture notes, Section 1.1)  February 8: Normed Spaces. Low Distortion Metric Embeddings.
normed spaces, Lipschitz maps, distortion, embeddings into L_{p} and l_{p} (see lecture notes)  February 13: Bourgain's Theorem
Bourgain's theorem  February 15: Sparsest Cut
approximation algorithm for Sparsest Cut (see lecture notes)  February 20: Minimum Balanced Cut, Minimum Linear Arrangement, Sparsest Cut with NonUniform demands. Expanders.
polylog approximation algorithms for Balanced Cut and Minimum Linear Arrangement, expander graphs, integrality gap for Sparsest Cut, Sparsest Cut with nonuniform demands  February 22: Minimum Multiway Cut, Minimum Multicut
approximation algorithms for Minimum Multiway Cut and Minimum Multicut (see lecture notes)  February 27: Padded Decomposition, Tree Metrics, Hierarchically Separated Trees (HST)
padded decomposition, HST (see lecture notes)  March 1: Padded Decomposition, Tree Metrics, Applications. Semidefinite Programming.
padded decomposition, HST, applications (see lecture notes), semidefinite programming  March 6: Dimension Reduction, Nearest Neighbor Search
dimension reduction, approximate nearest neighbor search, locality sensitive hashing  March 8: Locality Sensitive Hashing, pStable Random Variables
locality sensitive hashing, pstable random variables