### Course notes on random matrices

MATH 8380 Random Matrices (Fall 2019) • PDF course notesCourse page

### MATH 8380 • Random Matrices

[Fall 2019 semester]

### Course notes • Found typo or mistake? Let me know!

• Chapter 1. Some history
• Chapter 2. Gaussian Unitary Ensemble and Dyson Brownian Motion
• Interlude. Markov chains and stochastic differential equations
• Chapter 3. Two ways to derive the GUE eigenvalue distribution
• Chapter 4. Wigner Semicircle Law
• Chapter 5. Orbital measures and free operations
• Chapter 6. Representation-theoretic discrete analogue of the GUE
• Chapter 7. Determinantal point processes
• Chapter 8. Asymptotics via contour integrals
• Chapter 9. Harish-Chandra-Itsykson-Zuber integral and random matrix distributions
• Chapter 10. Universality
• Chapter 11. Markov maps on corners processes and generalizations

### Mapping TASEP back in time

[2019/07/04]

We obtain a new relation between the distributions $\mu_t$ at different times $t\ge 0$ of the continuous-time TASEP (Totally Asymmetric Simple Exclusion Process) started from the step initial configuration. Namely, we present a continuous-time Markov process with local interactions and particle-dependent rates which maps the TASEP distributions $\mu_t$ backwards in time. Under the backwards process, particles jump to the left, and the dynamics can be viewed as a version of the discrete-space Hammersley process. Combined with the forward TASEP evolution, this leads to an stationary Markov dynamics preserving $\mu_t$ which in turn brings new identities for expectations with respect to $\mu_t$.

The construction of the backwards dynamics is based on Markov maps interchanging parameters of Schur processes, and is motivated by bijectivizations of the Yang-Baxter equation. We also present a number of corollaries, extensions, and open questions arising from our constructions.

### 2020 travel

##### January

15-17 • Denver, CO • AMS Joint Mathematics Meeting

### Erratum to "Stochastic higher spin vertex models on the line"

[2019/05/19]

(PDF)

This is an erratum to the paper “Stochastic higher spin vertex models on the line”. The aim of the note is to address two separate errors in the paper: finite vertical spin Plancherel identities, and a false duality claim. The other main statements of the paper (the definition of new stochastic particle systems, duality relations for them, and contour integral observables) are not affected.

### Yang-Baxter random fields and stochastic vertex models

[2019/05/15]

Bijectivization refines the Yang-Baxter equation into a pair of local Markov moves which randomly update the configuration of the vertex model. Employing this approach, we introduce new Yang-Baxter random fields of Young diagrams based on spin $q$-Whittaker and spin Hall-Littlewood symmetric functions. We match certain scalar Markovian marginals of these fields with (1) the stochastic six vertex model; (2) the stochastic higher spin six vertex model; and (3) a new vertex model with pushing which generalizes the $q$-Hahn PushTASEP introduced recently by Corwin-Matveev-Petrov (2018). Our matchings include models with two-sided stationary initial data, and we obtain Fredholm determinantal expressions for the $q$-Laplace transforms of the height functions of all these models. Moreover, we also discover difference operators acting diagonally on spin $q$-Whittaker or (stable) spin Hall-Littlewood symmetric functions.

### From infinite random matrices over finite fields to square ice

Asymptotic representation theory of symmetric groups is a rich and beautiful subject with deep connections with probability, mathematical physics, and algebraic combinatorics. A one-parameter deformation of this theory related to infinite random matrices over a finite field leads to a randomization of the classical Robinson-Schensted correspondence between words and Young tableaux. Exploring such randomizations we find unexpected applications to six vertex (square ice) type models and traffic systems on a 1-dimensional lattice.