Title:Local Search Guarantees on Facility Location Problems
| Speaker: | Zishen Qu |
| Affiliation: | University of Waterloo |
| Room: | MC 5479 |
Abstract: We will show that guarantees can be given on algorithms for the k-median problem based on local search heuristics.
Title: Vertex Minors
| Speaker: | Jim Geelen |
| Affiliation: | University of Waterloo |
| Room: | MC 5501 |
Abstract:
I will give an introduction to vertex-minors of graphs. At first sight vertex minors are a bit peculiar but they are closely related to graph minors and possibly even easier to work with.
Title: Approximating k-Median via Pseudo-Approximation
| Speaker: | Sharat Ibrahimpur |
| Affiliation: | University of Waterloo |
| Room: | MC 5479 |
Abstract: In the last two talks we saw two approaches for approximating k-median.
Title: Razborov's flag algebras: Ten years on
| Speaker: | Sergey Norin |
| Affiliation: | McGill University |
| Room: | MC 5501 |
Abstract:
Many of the classical results in extremal combinatorics were obtained by ingenious application of elementary techniques, such as induction and Cauchy-Schwarz inequality.
Title: Hamiltonian Descent Methods
| Speaker: | Guojun Zhang |
| Affiliation: | University of Waterloo |
| Room: | MC 5479 |
Abstract: In this talk, I will present a paper on the Hamiltonian descent methods
Title: 2-universality of random graphs.
| Speaker: | Gal Kronenberg |
| Affiliation: | Tel Aviv University |
| Room: | *MC 6486* |
Abstract: For a family of graphs F, a graph G is F-universal if G contains every graph in F as a (not necessarily induced) subgraph.
Title: Approximate Clustering without the Approximation
| Speaker: | Thomas Baxter |
| Affiliation: | University of Waterloo |
| Room: | MC 5417 |
Abstract: Previously in this CombOpt Reading Group series, we have discussed improving approximation factors for particular distance-based objective functions in clustering problems.
Title: Approximation Algorithms for Distributionally Robust Stochastic Optimization
| Speaker: | Chaitanya Swamy |
| Affiliation: | University of Waterloo |
| Room: | MC 5501 |
Abstract:
Two-stage stochastic optimization is a widely-used framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios, and decisions are taken in two stages: we make first-stage decisions knowing only the underlying distribution and before a scenario is realized, and may take additional second-stage recourse actions after a scenario is realized.
Title: Why Random Reshuffling Beats Stochastic Gradient Descent
| Speaker: | Julian Romero |
| Affiliation: | University of Waterloo |
| Room: | MC 5479 |
Abstract: Over the first few lectures in the seminar we studied the Stochastic Gradient Descent (SGD) method
Title: A Short Proof of the Containers Theorem for Hypergraphs
| Speaker: | Michelle Delcourt |
| Affiliation: | University of Waterloo |
| Room: | MC 6486 |
Abstract: A modern trend in extremal combinatorics is extending classical results from the dense setting (e.g. Szemerédi's theorem)