Department of Mathematics,
University of California San Diego

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Math 211A: Seminar in Algebra

Dr. Karthik Ganapathy
UC San Diego

A Survey of Equivariant Noetherianity

Abstract:

I will discuss various noetherianity “up to symmetry” results from the literature and highlight some of their applications. I will then describe recent non-noetherianity phenomena in positive characteristic and explain how, perhaps unexpectedly, these are also connected to uniformity results in algebra.

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APM 7321

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Department of Mathematics,
University of California San Diego

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Math 243: Seminar in Functional Analysis

Konrad Aguilar
Pomona College

Christensen-Ivan spectral triples on AF algebras and Latrémolière's Gromov-Hausdorff propinquity

Abstract:

We provide convergence in the quantum Gromov-Hausdorff propinquity of Latrémolière of some sequences of infinite-dimensional Leibniz compact quantum metric spaces of Rieffel given by AF algebras and Christensen-Ivan spectral triples. The main examples are convergence of Effros-Shen algebras and UHF algebras. We will also present some of the results that laid the groundwork for this result. (This includes joint work with Clay Adams, Esteban Ayala, Evelyne Knight, and Chloe Marple, and this work is partially supported by NSF grant DMS-2316892).

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APM 6402

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Department of Mathematics,
University of California San Diego

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Math 269: Seminar in Combinatorics

John Byrne
University of Delaware

Nonabelian Sidon sets and extremal problems on digraphs

Abstract:

An $S_k$-set in a group $\Gamma$ is a set $A\subseteq\Gamma$ such that $\alpha_1\cdots\alpha_k=\beta_1\cdots\beta_k$ with $\alpha_i,\beta_i\in A$ implies $(\alpha_1,\ldots,\alpha_k)=(\beta_1,\ldots,\beta_k)$. An $S_k'$-set is a set such that $\alpha_1\beta_1^{-1}\cdots\alpha_k\beta_k^{-1}=1$ implies $\alpha_i=\beta_i$ or $\beta_i=\alpha_{i+1}$ for some $i$. We give constructions of large $S_k$-sets in the groups $\mathrm{Sym}(n)$ and $\mathrm{Alt}(n)$ and of large $S_2$-sets in $\mathrm{Sym}(n)\times\mathrm{Sym}(n)$ and $\mathrm{Alt}(n)\times\mathrm{Alt}(n)$. A probabilistic bound for `nice' groups obtains large $S_2'$-sets in $\mathrm{Sym}(n)$. We also give various upper bounds; in particular, if $k$ is even and $\Gamma$ has a normal abelian subgroup with bounded index then any $S_k$-set has size at most $(1-\varepsilon)|\Gamma|^{1/k}$.
   
 We describe some connections between $S_k$-sets and extremal graph theory. We determine up to a constant factor the minimum outdegree in a digraph with no subgraph in $\{C_{2,2},\ldots,C_{k,k}\}$, where $C_{\ell,\ell}$ is the orientation of $C_{2\ell}$ with two maximal directed $\ell$-paths. Contrasting with undirected cycles, the extremal minimum outdegree for $\{C_{2,2},\ldots,C_{k,k}\}$ is much smaller than that for any $C_{\ell,\ell}$. We count the directed Hamilton cycles in one of our constructions to improve the upper bound for a problem on Hamilton paths introduced by Cohen, Fachini, and Körner.

This talk is based on joint work with Michael Tait; see https://arxiv.org/abs/2509.07750.

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APM 7321

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Department of Mathematics,
University of California San Diego

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Math 278C: Optimization and Data Science

Prof. Robert Webber
UCSD

Randomly sparsified Richardson iteration: A dimension-independent sparse linear solver

Abstract:

 

Recently, a class of algorithms combining classical fixed-point iterations with repeated random sparsification of approximate solution vectors has been successfully applied to eigenproblems with matrices as large as . So far, a complete mathematical explanation for this success has proven elusive. The family of methods has not yet been extended to the important case of linear system solves. In this paper, we propose a new scheme based on repeated random sparsification that is capable of solving sparse linear systems in arbitrarily high dimensions. We provide a complete mathematical analysis of this new algorithm. Our analysis establishes a faster-than-Monte Carlo convergence rate and justifies use of the scheme even when the solution is too large to store as a dense vector.

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APM 2402 & Zoom (Meeting ID: 926 5846 1639 / Password: OPT25FA)

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Department of Mathematics,
University of California San Diego

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Final Defense

Yubo Shuai
UCSD

The site frequency spectrum in population models

Abstract:

The site frequency spectrum is a commonly used statistic to summarize the mutational data in a sample from the population. In this talk, we will consider the site frequency spectrum for populations growing exponentially or under spatial constraints. I will also briefly discuss some applications to biological data.

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APM 6402

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Department of Mathematics,
University of California San Diego

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Math 278B: Mathematics of Information, Data, and Signals

TBA

TBA

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APM 6402

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