Office: FEC 3120, phone: 277-5446 The best way to reach me for this class is
generally via Piazza. I will check it once a day, usually around noon.
Office Hours: 1:45-3:00pm or by appointment.
Note: I will always be available in my office during office hours. At other times, if my door is open, feel free to come
in. If the door is closed, I'm probably at work on a paper, grant or research problem. Please come by another time or make an appointment via email.
The class meets 12:30-1:45 T/TH in CENT 1032.
This course will cover mathematical topics in Geometric Methods in Computer Science, with an eye towards modern applications (e.g. machine learning, big data, distributed computing).
The methodology will be mathematical i.e. theorems and proofs.
U. Maryland Notes, Pages 41-44 give a good connection between convex hulls,
and upper/lower envelopes, Lecture 8 gives good connection between envelopes and linear programming. Lecture 16 gives good connections between convex hulls
and Voronoi diagrams, and Delaunay triangulations
Voronoi Diagrams, Delaunay Triangulations and More Dual Transformations
MIT Algorithm's Projects This is a general description of how to find a good
CS theory project. The specific project ideas in this class are, of course, different from our own class - if you'd like specific ideas, please talk to me.
Convex Optimization by Boyd and Vandenberghe Particularly of interest: Section 2.3 "Operations that Preserve Convexity" ;
Chapter 4 and onward discuss optimization algorithms (albeit informally, without proofs of convergence time; NB that many problems discussed (i.e. quadratic programming) are NP-Hard).
See Vishnoi's notes above for a more formal treatment.