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Please note: The University of Waterloo is closed for all events until further notice.

Events tagged with Future graduate students

Thursday, June 4, 2020 — 2:30 PM EDT

Title: Weighted generating functions for weighted chord diagrams

Speaker: Lukas Nabergall
Affiliation: University of Waterloo
Zoom: Contact Karen Yeats

Abstract:

Motivated by the universal property of the Connes-Kreimer Hopf algebra of rooted trees and Hopf subalgebras arising from so-called combinatorial Dyson-Schwinger equations, we introduce a class of two-variable recursive functional equations involving Hochschild 1-cocycle operators.

Friday, June 5, 2020 — 1:00 PM EDT

Title: A Strongly Polynomial Label-Correcting Algorithm for Linear Systems with Two Variables per Inequality

Speaker: Cedric Koh
Affiliation: London School of Economics and Political Science
Zoom: Contact Sharat Ibrahimpur

Abstract:

In this talk, I will present a strongly polynomial label-correcting algorithm for solving the feasibility of linear systems with two variables per inequality. The algorithm is based on the Newton–Dinkelbach method for fractional combinatorial optimization, and extends previous work of Madani (2002).

Friday, June 5, 2020 — 3:30 PM EDT

Lauren K. Williams

Title: Matroids, tropical geometry, and positivity

Speaker: Lauren K. Williams
Affiliation: Harvard University & Radcliffe Institute
Zoom: Please email Emma Watson.

Abstract: 

The theory of matroids -- a class of combinatorial objects which simultaneously generalize graphs as well as vectors in a vector space -- was pioneered by William Tutte in his 1948 PhD thesis. Matroids are also closely connected to the Grassmannian and the tropical Grassmannian.  In recent years, mathematicians and physicists have been exploring positive notions of all of these objects, finding applications to scattering amplitudes and shallow water waves.  In my talk I will give an introduction to matroids, tropical geometry, and positivity, and survey some of the beautiful results and applications.

Friday, June 12, 2020 — 3:30 PM EDT

Title: Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis

Speaker: Courtney Paquette
Affiliation: University of Waterloo
Zoom: Please email Emma Watson

Abstract:

Average-case analysis computes the complexity of an algorithm averaged over all possible inputs. Compared to worst-case analysis, it is more representative of the typical behavior of an algorithm, but remains largely unexplored in optimization. One difficulty is that the analysis can depend on the probability distribution of the inputs to the model.

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