The Combinatorics and Optimization square: A history
Contact Info
Combinatorics & Optimization
University of Waterloo
Waterloo, Ontario
Canada N2L 3G1
Phone: 519-888-4567, ext 33038
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Welcome to Combinatorics and Optimization
The C&O department has 33 faculty members and 60 graduate students. We are intensely research oriented and hold a strong international reputation in each of our six major areas:
- Algebraic combinatorics
- Combinatorial optimization
- Continuous optimization
- Cryptography
- Graph theory
- Quantum computing
Read more about the department's research to learn of our contributions to the world of mathematics!
Tutte's Distinguished Lecture Series
The very successful Tutte's 100th Distinguished Lecture Series has now completed. That success has led to a Tutte Distinguished Lecture once per term. The next lecture will happen in the Spring term.
*Recordings of occurred talks are all available on C&O's YouTube Channel.
Grad Studies: Fall 2020 applications now open
New Deadline: February 1, '20
News
- June 11, 20212021 Governor General’s Academic Silver Medal awarded to Robert Cummings
Robert Cummings is the recipient of the 2021 Governor General’s Academic Silver Medal for the Faculty of Mathematics. Cummings, an Honours Computer Science and Combinatorics and Optimization student, will graduate this June with a BMath.
- May 21, 2021Stephen Melczer's book on Analytic Combinatorics has been published
"An Invitation to Analytic Combinatorics: From One to Several Variables", written by C&O professor Stephen Melczer, has been published by Springer.
- May 20, 2021Spring 2021 convocation
Three Ph.D. and seven M.Math. C&O students will receive their degrees at the Spring 2021 convocation on June 18.
Events
- June 25, 2021Tutte Colloquium - Dmitriy Drusvyatskiy
Title: From low probability to high confidence in stochastic convex optimization
Speaker: Dmitriy Drusvyatskiy Affliliation: University of Washington Zoom: Contact Emma Watson Abstract:
Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. More nuanced high probability guarantees are rare, and typically either rely on “light-tail” noise assumptions or exhibit worse sample complexity. In this work, we show that a wide class of stochastic optimization algorithms can be augmented with high confidence bounds at an overhead cost that is only logarithmic in the confidence level and polylogarithmic in the condition number.
- June 28, 2021Algebraic Graph Theory Seminar - Steve Kirkland
Title: State transfer for paths with weighted loops at the end vertices
Speaker: Steve Kirkland Affiliation: University of Manitoba Zoom: Contact Soffia Arnadottir Abstract:
We consider a continuous time quantum walk on an unweighted path on n vertices, to which a loop of weight w has been added at each end vertex. Let f(t) denote the fidelity of state transfer from one end vertex to the other at time t. In this talk we give upper and lower bounds on f(t) in terms of w, n and t; further, given a > 0 we discuss the values of t for which f(t) > 1-a.