Georgia Institute of Technology College of Sciences

We'll begin with a primer on hyperbolic and stable polynomials, which have been popular in recent years due to their many surprising appearances in combinatorics and algebra. We will cover a sketch of the famous Branden Borcea characterization of univariate stability preservers in the first part of the talk. We will then discuss more our recent work on multivariate hyperbolic polynomials which are invariant under permutations of their variables and connections to this Branden Borcea characterization.

Zoom Link: https://gatech.zoom.us/j/99596774152

The Anderson tight binding model describes an electron moving in a disordered material. Such models are, depending on various parameters of the system, either expected to or known to display a phenomenon known as Anderson localization, in which this disorder can "trap" electrons. Different versions of this phenomenon can be characterized spectrally or locally. We will review both the dominant methods and some of the foundational results in the study of these systems in arbitrary dimension, before shifting our focus to aspects of the one-dimensional theory.

 

Specifically, we will examine the transfer matrix method, which allows us to leverage the Furstenberg theory of random matrix products to understand the asymptotics of generalized eigenfunctions. From this, we will briefly sketch a proof of localization given originally in Jitomirskaya-Zhu (2019). Finally, we will discuss recent work of the speaker combining the argument in Jitomirskaya-Zhu with certain probabilistic results to prove localization for a broader class of models.

The edge-coloring problem (ECP) for a multigraph $G=(V, E)$ is to color its edges with minimum number of colors such that no two adjacent vertices receive the same color. ECP can be naturally formulated as an integer program, and its linear programming relaxation is referred to as the fractional edge-coloring problem (FECP). The optimal value of ECP (resp. FECP) is called the chromatic index (resp. fractional chromatic index) of $G$, denoted by $\chi^{\prime}(G)$ (resp. $\chi^*(G)$). Let $\Delta(G)$ be the maximum degree of $G$ and let $ \mathcal{W}^*(G) $ be the fractional density of $G$, defined by $$ \mathcal{W}^*(G) = \max _{U \subseteq V,|U| \geq 2}\frac{|E(U)|}{\lfloor|U|/2\rfloor}. $$ Seymour showed that $\chi^*(G)=\max \{\Delta(G), \mathcal{W}^*(G)\}$. Moreover, the Goldberg-Seymour Conjecture is confirmed Chen, Jing, and Zang states that $\chi^{\prime}(G) \leq \max \{\Delta(G)+1,\lceil\mathcal{W}^*(G)\rceil\}$. Chen, Zang and Zhao developed an algorithm that calculates $ \mathcal{W}^*(G) $ in strongly polynomial time. Inspired by their results, we consider the fractional $ f $-edge-coloring problem ($ f $-FECP) for a given function $ f:V\to \mathbb N_+ $, which is a generalization of FECP: each spanning subgraph induced by a color class has degree at most $ f(v) $ at each vertex $ v\in V $. We give a strongly polynomial-time algorithm for calculating the corresponding fractional $ f $-density $$ \mathcal{W}^*_{f}(G)=\max _{U \subseteq V,|U| \geq 2}\frac{|E(U)|}{\lfloor f(U) / 2\rfloor}. $$

We will discuss the work of Ding-Smart (2019) which showed Anderson localization at the bottom of the spectrum for random discrete Schroedinger operators with arbitrary bounded noise, i.e. without any supposition of regularity of the distribution. In this talk, we will discuss at a high level the basic idea behind a multi-scale analysis, as well as the usual ingredients involved in one: resolvent decay at large scales and the Wegner-type estimate.

We will then discuss the obstacles posed by singular distributions, and the various methods used to overcome these obstacles in various regimes, discussing briefly the transfer matrix method used for d=1 by Carmona-Klein-Martinelli (1987) before examining the unique continuation principles used by Bourgain-Kenig (2005) and the Ding-Smart work which are used in d=2 in the continuum and discrete cases respectively, highlighting the unique challenges arising in the discrete case.