CS388R: Randomized Algorithms (Fall 2021)

Logistics: Tue/Thu 2:00 - 3:30
GDC 2.210
Course web page: http://www.cs.utexas.edu/~ecprice/courses/randomized/fa21/
Professor: Eric Price
Email: ecprice@cs.utexas.edu
Office: GDC 4.510
Office Hours: Wednesday 3:30-4:30pm over Zoom.
TA: Ruizhe Zhang ruizhe@utexas.edu
Office Hours: Monday 2-3 over Zoom
Wednesday 2-3 TA station desk 4.
Useful References: Previous offerings (2015, 2017, 2019) have relevant information. Similar courses are offered at MIT and Berkeley.
Course format: This course will be fully in-person.
Lecture Schedule:
DateTopicScribe notesHW
August 26 Introduction; min-cut Notes 1 HW 1 due Sep 2
August 31 Concentration Inequalities Notes 2
September 2 Quicksort Notes 3
September 7 Game Tree Evaluation Notes 4 HW 2 due Sep 14
September 9 Balls and Bins Notes 5
September 14 Power of Two Choices Notes 6 HW 3 due Sep 21
September 16 Cuckoo Hashing Notes 7
September 21 Bloom filters Notes 8
September 23 Limited Independence Notes 9 HW 4 due Sep 30
September 28 Routing Notes 10
September 30 Fingerprinting Notes 11 HW 5 due Oct 7
October 5 All-pairs shortest path Notes 12
October 7 Sampling, median-finding Notes 13
October 12 Midterm exam
October 14 Maximum perfect matchings Notes 14 HW 6 due Oct 21
October 19 Online Bipartite Matching Notes 15
October 21 Matrix concentration and graph sparsification Notes 16 HW 7 due Oct 28
October 26 Spectral sparsification of graphs Notes 17
October 28 Markov Chains I Notes 18
November 2 Markov Chains II; Closest Pair Notes 19 HW 8 due November 9
November 4 Computational geometry II Notes 20
November 9 Nearest Neighbor Search Notes 21
November 11 Network coding Notes 22 HW 9 due November 18
November 16 Randomized numerical linear algebra I Notes 23
November 18 Randomized numerical linear algebra II Notes 24
November 23 Randomized Rounding Notes 25
November 30 Review Notes 26
December 2 Final exam
Content: This graduate course will study the use of randomness in algorithms. Over the past thirty years, randomization has become an increasingly important part of theoretical computer science.
Prerequisites: Mathematical maturity and comfort with undergraduate algorithms and basic probability.
Grading: 40%: Homework
20%: Final exam
20%: Midterm exam
20%: Scribing lectures
Scribing: In each class, two students will be assigned to take notes. These notes should be written up in a standard LaTeX format before the next class.
Text: You may find the text Randomized Algorithms by Motwani and Raghavan to be useful, but it is not required.
There will be a homework assignment every week.

Collaboration policy: You are encouraged to collaborate on homework. However, you must write up your own solutions. You should also state the names of those you collaborated with on the first page of your submission.
Students with
Any student with a documented disability (physical or cognitive) who requires academic accommodations should contact the Services for Students with Disabilities area of the Office of the Dean of Students at 471-6259 (voice) or 471-4641 (TTY for users who are deaf or hard of hearing) as soon as possible to request an official letter outlining authorized accommodations.