Randomized Algorithms

CS388R: Randomized Algorithms (Fall 2023)

Logistics: Mon/Wed 2:00 - 3:30
GDC 2.210
Course web page: http://www.cs.utexas.edu/~ecprice/courses/randomized/fa23/

Professor: Eric Price
Email: ecprice@cs.utexas.edu
Office: GDC 4.510
Office Hours: Monday 3:30-4:30pm.

TA: Michael Jaber michaeljohnjaber@gmail.com

Useful References: Previous offerings (2015, 2017, 2019, 2021) have relevant information. Similar courses are offered at MIT and Berkeley.

Course format: This course will be fully in-person.

Lecture Schedule:

Date	Topic	Scribe notes	HW
August 21	Introduction; min-cut	Notes 1
August 23	Concentration Inequalities	Notes 2	HW 1
August 28	More min-cut, coin bias estimation	Notes 3
August 30	Game Tree Evaluation	Notes 4	HW 2
	(Labor day)
September 6	Balls and Bins	Notes 5
September 11	Power of Two Choices	Notes 6
September 13	Cuckoo Hashing	Notes 7	HW 3
September 18	Bloom Filters	Notes 8
September 20	Limited Independence	Notes 9	HW 4
September 25	Routing	Notes 10
September 27	Fingerprinting	Notes 11
October 2	All-pairs shortest path	Notes 12	HW 5
October 4	Sampling, median-finding	Notes 13
October 9	Midterm exam
October 11	Maximum perfect matchings	Notes 14	HW 6
October 16	Online bipartite matching	Notes 15
October 18	Matrix concentration and graph sparsification	Notes 16	HW 7
October 23	Concentration Inequalities Revisited	Notes 17
October 25	Spectral sparsification of graphs	Notes 18	HW 8
October 30	Markov Chains I	Notes 19
November 1	Markov Chains II; start computational geometry	Notes 20	HW 9
November 6	Computational geometry	Notes 21
November 8	Nearest Neighbor Search	Notes 22
November 13	Network Coding	Notes 23
November 15	Randomized numerical linear algebra I	Notes 24	HW 10
	(Thanksgiving week)
November 27	Randomized numerical linear algebra II	Notes 25
November 29	Randomized Rounding	Notes 26
December 4	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.

Homework
policy: 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
Disabilites: If you are a student with a disability, or think you may have a disability, and need accommodations please contact Disability and Access (D&A). You may refer to D&A's website for contact and more information. If you are already registered with D&A, please deliver your Accommodation Letter to me as early as possible in the semester so we can discuss your approved accommodations.