Schur--Weyl duality for diagonalizing a Markov chain on the hypercube
Persi Diaconis, Andrew Lin, Arun Ram
TL;DR
The paper develops an explicit, orthonormal eigenbasis for the Burnside Markov chain on the binary hypercube by leveraging Schur–Weyl duality and an $\mathfrak{sl}_2$-action to decompose $V^{\otimes n}$ into isotypic components labeled by Young tableaux. It introduces the $g_Q^{m,\ell}$ and $f_Q^{m,\ell}$ vectors that span irreducible subspaces and are eigenvectors of the transition operator, with eigenvalues determined by $m+\ell$ and independent of $K_n$. The resulting framework yields exact norm formulas, links to Hahn and Chebyshev polynomials, and sharp mixing-time bounds, including a bounded-time convergence from the one-one state $e_n$ in $\ell^2$ and $\ell^1$. The approach also clarifies eigenvalue multiplicities across irreducible components and opens avenues for generalizations to other $(C_k^n, S_n)$ Burnside processes. Overall, the work provides a principled, algebraic route to precise convergence analyses in highly symmetric Markov chains.
Abstract
We show how the tools of modern algebraic combinatorics -- representation theory, Murphy elements, and particularly Schur--Weyl duality -- can be used to give an explicit orthonormal basis of eigenfunctions for a "curiously slowly mixing Markov chain" on the space of binary $n$-tuples. The basis is used to give sharp rates of convergence to stationarity.
