STRUCTURED INVERSION OF THE BERNSTEIN-VANDERMONDE MATRIX

Bernstein polynomials, long a staple of approximation theory and computational geometry, have also increasingly become of interest in finite element methods. Many fundamental problems in interpolation and approximation give rise to interesting linear algebra questions. When attempting to find a polynomial approximation of boundary or initial data, one encounters the Bernstein-Vandermonde matrix, which is found to be highly ill conditioned. In [L. Allen and R. C. Kirby, SIAM J. Matrix Anal. Appl., 41 (2020), pp. 413-431], we used the relationship between monomial Bezout matrices and the inverse of Hankel matrices to obtain a decomposition of the inverse of the Bernstein mass matrix in terms of Hankel, Toeplitz, and diagonal matrices. In this paper, we use properties of the Bernstein-Bezout matrix to factor the inverse of the Bernstein-Vandermonde matrix into a difference of products of Hankel, Toeplitz, and diagonal matrices. We also use the nonstandard matrix norm defined in [L. Allen and R. C. Kirby, SIAM J. Matrix Anal. Appl., 41 (2020), pp. 413-431] to study the conditioning of the Bernstein-Vandermonde matrix, showing that the conditioning in this case is better than in the standard 2 norm. Additionally, we use properties of multivariate Bernstein polynomials to derive a block LU decomposition of the Bernstein-Vandermonde matrix corresponding to equispaced nodes on the d-simplex.