🤖 AI Summary
This work proposes an efficient algorithm for computing the leading principal submatrices of the Hermite normal form (HNF) of polynomial matrices endowed with displacement structure, addressing the high computational cost associated with full HNF computation. By integrating displacement structure with an evaluation–interpolation framework, the method recovers selected rows from the matrix inverse and derives the target submatrix via relation bases. This study presents the first fusion of displacement structure techniques with the evaluation–interpolation paradigm, achieving a significant reduction in computational complexity compared to conventional full HNF algorithms. The approach is particularly well-suited for applications involving Gröbner bases and bivariate polynomials, where structured polynomial matrices commonly arise.
📝 Abstract
Following several decades of successive algorithmic improvements, works from the 2010s have showed how to compute the Hermite normal form (HNF) of a univariate polynomial matrix within a complexity bound which is essentially that of polynomial matrix multiplication. Recently, several results on bivariate polynomials and Gr\"obner bases have highlighted the interest of computing determinants or HNFs of polynomial matrices that happen to be structured, with a small displacement rank. In such contexts, a small leading principal submatrix of the HNF often contains all the sought information. In this article, we show how the displacement structure can be exploited in order to accelerate the computation of such submatrices. To achieve this, we rely on structured linear algebra over the field thanks to evaluation-interpolation. This allows us to recover some rows of the inverse of the input matrix, from which we deduce the sought HNF submatrix via bases of relations.