Representations of the symmetric group are decomposable in polynomial time

TL;DR

This paper presents a polynomial-time algorithm for decomposing orthogonal matrix representations of the symmetric group into irreducible components, improving computational efficiency for representation analysis.

Contribution

It introduces a new algorithm that efficiently decomposes symmetric group representations and computes irreducible multiplicities, with practical improvements over existing methods.

Findings

Algorithm has complexity $O(n^2 d^3)$ for identifying irreducible components.

Practical implementation shows better complexity using floating point arithmetic.

Effective for tensor product multiplicities and constructing orthogonal polynomial bases.

Abstract

We introduce an algorithm to decompose orthogonal matrix representations of the symmetric group over the reals into irreducible representations, which as a by-product also computes the multiplicities of the irreducible representations. The algorithm applied to a $d$-dimensional representation of $S_n$ is shown to have a complexity of $O(n^2 d^3)$ operations for determining which irreducible representations are present and their corresponding multiplicities and a further $O(n d^4)$ operations to fully decompose representations with non-trivial multiplicities. These complexity bounds are pessimistic and in a practical implementation using floating point arithmetic and exploiting sparsity we observe better complexity. We demonstrate this algorithm on the problem of computing multiplicities of two tensor products of irreducible representations (the Kronecker coefficients problem) as well as higher order tensor products. For hook and hook-like irreducible representations the algorithm has polynomial complexity as $n$ increases. We also demonstrate an application to constructing a basis of multivariate orthogonal polynomials with respect to a tensor product weight so that applying a permutation of variables induces an irreducible representation.