Heegner points are a powerful tool for understanding the structure of the group of rational points on elliptic curves. In this talk, I will describe these points and the ideas surrounding their generalisation to other situations.
We give an exponential improvement on the upper bound for the $r$-colour diagonal Ramsey number for all $r$. The proof relies on geometric insights and offers a simplified proof in the case of $r=2$.
Joint Work with: Paul Ballister, Béla Bollobás, Marcelo Campos, Simon Griffiths, Rob Morris, Julian Sahasrabudhe and Marius Tiba.
We show that an $n$-vertex $k$-uniform hypergraph, where all $(k-1)$-subsets that are supported by an edge are in fact supported by at least $n/2+o(n)$ edges, contains a spanning $(k-1)$-dimensional sphere. This generalises Dirac's theorem, and confirms a conjecture of Georgakopoulos, Haslegrave, Montgomery, and Narayanan. Unlike typical results in the area, our proof does not rely on the absorption method or the regularity lemma. Instead, we use a recently introduced framework that is based on covering the vertex set of the host hypergraph with a family of complete blow-ups.
This is joint work with Freddie Illingworth, Richard Lang, Olaf Parczyk, and Amedeo Sgueglia.
Given an elliptic curve over the rationals, a natural problem is to find an explicit point of infinite order over a given number field when there is expected to be one. Geometric constructions are known in only two different settings. That of Heegner points, developed since the 1950s, which yields points over abelian extensions of imaginary quadratic fields. And that of Stark-Heegner points, from the late 1990s: here the points constructed are conjectured to be defined over abelian extensions of real quadratic fields. I will describe a new analytic formula which encompasses both of these, and conjecturally yields points in many other settings. This is joint work with Henri Darmon and Victor Rotger.
Multicomponent flows, i.e. mixtures, can be modeled effectively using the Onsager-Stefan-Maxwell (OSM) equations. The OSM equations can account for a wide variety of phenomena such as diffusive, convective, non-ideal mixing, thermal, pressure and electrochemical effects for steady and transient multicomponent flows. I will first introduce the general OSM framework and a finite element discretisation for multicomponent diffusion of ideal gasses. Then I will show two ways of preconditioning the multicomponent diffusion problem for various boundary conditions. Time permitting, I will also discuss how this can be extended to the non-ideal, thermal, and nonisobaric settings.
Tensor methods are methods for unconstrained continuous optimization that can incorporate derivative information of up to order p > 2 by computing a step based on the pth-order Taylor expansion at each iteration. The most important among them are regularization-based tensor methods which have been shown to have optimal worst-case iteration complexity of finding an approximate minimizer. Moreover, as one might expect, this worst-case complexity improves as p increases, highlighting the potential advantage of tensor methods. Still, the global complexity results only guarantee pessimistic sublinear rates, so it is natural to ask how local rates depend on the order of the Taylor expansion p. In the case of strongly convex functions and a fixed regularization parameter, the answer is given in a paper by Doikov and Nesterov from 2022: we get pth-order local convergence of function values and gradient norms.
The value of the regularization parameter in their analysis depends on the Lipschitz constant of the pth derivative. Since this constant is not usually known in advance, adaptive regularization methods are more practical. We extend the local convergence results to locally strongly convex functions and fully adaptive methods.
We discuss how for p > 2 it becomes crucial to select the "right" minimizer of the regularized local model in each iteration to ensure all iterations are eventually successful. Counterexamples show that in particular the global minimizer of the subproblem is not suitable in general. If the right minimizer is used, the pth-order local convergence is preserved, otherwise the rate stays superlinear but with an exponent arbitrarily close to one depending on the algorithm parameters.
The Parker conjecture, which explores whether magnetic fields in perfectly conducting plasmas can develop tangential discontinuities during magnetic relaxation, remains an open question in astrophysics. Helicity conservation provides a topological barrier against topologically nontrivial initial data relaxing to a trivial solution. Preserving this mechanism is therefore crucial for numerical simulation.
This paper presents an energy- and helicity-preserving finite element discretization for the magneto-frictional system for investigating the Parker conjecture. The algorithm enjoys a discrete version of the topological mechanism and a discrete Arnold inequality.
We will also discuss extensions to domains with nontrivial topology.
This is joint work with Prof Patrick Farrell, Dr Kaibo Hu, and Boris Andrews
In Bayesian inverse problems, it is common to consider several hyperparameters that define the prior and the noise model that must be estimated from the data. In particular, we are interested in linear inverse problems with additive Gaussian noise and Gaussian priors defined using Matern covariance models. In this case, we estimate the hyperparameters using the maximum a posteriori (MAP) estimate of the marginalized posterior distribution.
However, this is a computationally intensive task since it involves computing log determinants. To address this challenge, we consider a stochastic average approximation (SAA) of the objective function and use the preconditioned Lanczos method to compute efficient function evaluation approximations.
We can therefore compute the MAP estimate of the hyperparameters efficiently by building a preconditioner which can be updated cheaply for new values of the hyperparameters; and by leveraging numerical linear algebra tools to reuse information efficiently for computing approximations of the gradient evaluations. We demonstrate the performance of our approach on inverse problems from tomography.
Von Neumann algebras which are not matrix algebras, yet still possess a unique trace, form a basic class called II$_1$ factors. The set of asymptotically commuting elements (or, the relative commutant of the algebra within its own ultrapower), dubbed the central sequence algebra, can take many different forms. In this talk, we discuss an elementary class of II$_1$ factors whose central sequence algebra is again a II$_1$ factor. We show that the class of infinitely generic II$_1$ factors possess this property, and ask some related questions about properties of other existentially closed II$_1$ factors. This is based on joint work with Isaac Goldbring, David Jekel, and Srivatsav Kunnawalkam Elayavalli.
The widespread need to solve large-scale linear systems has sparked a growing interest in randomized techniques. One such class of techniques is known as iterative random sketching methods (e.g., Randomized Block Kaczmarz and Randomized Block Coordinate Descent). These methods "sketch" the linear system to generate iterative, easy-to-compute updates to a solution. By working with sketches, these methods can often enable more efficient memory operations, potentially leading to faster performance for large-scale problems. Unfortunately, tracking the progress of these methods still requires computing the full residual of the linear system, an operation that undermines the benefits of the solvers. In practice, this cost is mitigated by occasionally computing the full residual, typically after an epoch. However, this approach sacrifices real-time progress tracking, resulting in wasted computations. In this talk, we use statistical techniques to develop a progress estimation procedure that provides inexpensive, accurate real-time progress estimates at the cost of a small amount of uncertainty that we effectively control.