A block Hankel generalized confluent Vandermonde matrix
Andre Klein, Peter Spreij

TL;DR
This paper introduces a generalization of confluent Vandermonde matrices using block Hankel structures derived from derivatives of specific matrix functions, analyzing their algebraic properties with elementary methods.
Contribution
It defines and studies a new class of block Hankel matrices based on derivatives of matrix products, providing insights into their rank, null-space, and inverses without relying on advanced matrix polynomial theory.
Findings
Determined the rank of the generalized block Hankel matrices.
Characterized the null-space and its parametrization.
Derived conditions for the existence of inverses.
Abstract
Vandermonde matrices are well known. They have a number of interesting properties and play a role in (Lagrange) interpolation problems, partial fraction expansions, and finding solutions to linear ordinary differential equations, to mention just a few applications. Usually, one takes these matrices square, say, in which case the -th column is given by , where we write . If all the () are different, the Vandermonde matrix is non-singular, otherwise not. The latter case obviously takes place when all are the same, say, in which case one could speak of a confluent Vandermonde matrix. Non-singularity is obtained if one considers the matrix whose -th column () is given by the -th derivative . We will consider generalizations of the confluent Vandermonde matrix…
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