Symmetric functions and vertex models
A symmetric function is, roughly, a function of variables (x_1,x_2, ....) that is unchanged when the variables are permuted.
For example,
x_1 + x_2 + x_3,
x_1 x_2 + x_1 x_3 + x_2x_3,
x_1 x_2 x_3
are symmetric.
The subject began with polynomial equations: the coefficients are the elementary symmetric functions of the roots are the elementary symmetric functions of the roots.
But eventually it became a common language connecting
algebra(representation of symmetric group, characters of representations of many algebras),
combinatorics(Young tableaux, plane partitions, permutations, lattice paths and tilings),
geometry and topology(cohomology of Grassmannians, Schubert calculus, cohomology and \(K\)-theory of flag varieties),
probability(random matrices, random tilings and plane partitions, interacting particle systems such as TASEP and \(q\)-TASEP;
asymptotic representation theory, KPZ universality, Schur measures and Schur processes),
and mathematical physics(tau functions of the integrable hierarchies, eigenfunctions of quantum many-body systems, partition functions of vertex models).
During the nineteenth century, symmetric functions became objects of study in their own right.
In the 20th century the subject was absorbed into algebraic combinatorics and representation theory, and then by algebraic geometry.
Over the last half century, symmetric functions have become an important part of mathematical physics and probability theory.
Nowadays, an active area of research in symmetric functions involves studying them through certain vertex models—statistical physics models that possess integrability properties.
This connection allows to prove properties and identities for symmetric functions in an elegant way,
finding new properties of symmetric functions, defining new classes of symmetric functions, and also investigating the properties of the latter.
In this course, I want to show how the theory of vertex models naturally becomes a generating theory for the theory of symmetric functions. In the sense that simple and natural identities for the former generate properties and identities for the latter.
The main examples of symmetric functions we will consider are Schur polynomials, Grothendieck polynomials, and Hall-Littlewood polynomials, which relate to and 5- and 6-vertex models on the vertex models side.
For example,
x_1 + x_2 + x_3,
x_1 x_2 + x_1 x_3 + x_2x_3,
x_1 x_2 x_3
are symmetric.
The subject began with polynomial equations: the coefficients are the elementary symmetric functions of the roots are the elementary symmetric functions of the roots.
But eventually it became a common language connecting
algebra(representation of symmetric group, characters of representations of many algebras),
combinatorics(Young tableaux, plane partitions, permutations, lattice paths and tilings),
geometry and topology(cohomology of Grassmannians, Schubert calculus, cohomology and \(K\)-theory of flag varieties),
probability(random matrices, random tilings and plane partitions, interacting particle systems such as TASEP and \(q\)-TASEP;
asymptotic representation theory, KPZ universality, Schur measures and Schur processes),
and mathematical physics(tau functions of the integrable hierarchies, eigenfunctions of quantum many-body systems, partition functions of vertex models).
During the nineteenth century, symmetric functions became objects of study in their own right.
In the 20th century the subject was absorbed into algebraic combinatorics and representation theory, and then by algebraic geometry.
Over the last half century, symmetric functions have become an important part of mathematical physics and probability theory.
Nowadays, an active area of research in symmetric functions involves studying them through certain vertex models—statistical physics models that possess integrability properties.
This connection allows to prove properties and identities for symmetric functions in an elegant way,
finding new properties of symmetric functions, defining new classes of symmetric functions, and also investigating the properties of the latter.
In this course, I want to show how the theory of vertex models naturally becomes a generating theory for the theory of symmetric functions. In the sense that simple and natural identities for the former generate properties and identities for the latter.
The main examples of symmetric functions we will consider are Schur polynomials, Grothendieck polynomials, and Hall-Littlewood polynomials, which relate to and 5- and 6-vertex models on the vertex models side.
讲师
日期
2026年09月15日 至 12月10日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二,周四 | 13:30 - 15:05 | A3-3-301 | ZOOM 04 | 482 240 1589 | BIMSA |
修课要求
Linear algebra and abstract algebra, including some familiarity with symmetric group. Probability theory at the level of a first undergraduate course will also be useful. No previous knowledge of symmetric functions, Young tableaux, vetrex models will be assumed, but it is very welcome.
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
视频公开
公开
笔记公开
公开
讲师介绍
Andrii Liashyk的研究领域是可积系统,主要研究量子系统。他于2020年获得了Skoltech高等研究中心的博士学位。2022年,他加入BIMSA担任助理教授。