Speaker: Dr. Chul-hee Lee (KIAS)
Date: 16:30 - 17:30, May 17, 2019
Place: AORC Seminar Room (SKKU, General Studies Bd. 3F)
Title : Linear regression for computing with Macdonald polynomials
Abstract :
Linear regression is a statistical method for estimating the linear relationships among variables. It is widely used in many areas of science but is not very common to pure mathematicians. While doing some heavy computations related to Macdonald polynomials, I found a way to apply linear regression to a problem in symmetric polynomials. It is sometimes possible to find a coefficient for a symmetric polynomial expanded in the Macdonald basis, supposed to be in product form, using numerical linear regression at a much lower computational cost than symbolic methods. In this talk, I will explain how linear regression can be included in the toolbox of a researcher in algebraic combinatorics.