### authors

- MARTINEZ DOPICO, FROILAN CESAR
- PEREZ, JAVIER
- VAN DOOREN, PAUL

- February 2018

- 163

- 204

- 562

- 0024-3795

- 1873-1856

- The standard way of solving a polynomial eigenvalue problem associated with a matrix polynomial starts by embedding the matrix coefficients of the polynomial into a matrix pencil, known as a strong linearization. This process transforms the problem into an equivalent generalized eigenvalue problem. However, there are some situations in which is more convenient to replace linearizations by other low degree matrix polynomials. This has motivated the idea of a strong t-ification of a matrix polynomial, which is a matrix polynomial of degree at most P having the same finite and infinite elementary divisors, and the same numbers of left and right minimal indices as the original matrix polynomial. We present in this work a novel method for constructing strong P-ifications of matrix polynomials of size m x n and grade d when l < d, and l divides nd or md. This method is based on a family called "strong block minimal bases matrix polynomials", and relies heavily on properties of dual minimal bases. We show how strong block minimal bases l-ifications can be constructed from the coefficients of a given matrix polynomial P(lambda). We also show that these t-ifications satisfy many desirable properties for numerical applications: they are strong t-ifications regardless of whether P(lambda) is regular or singular, the minimal indices of the l-ifications are related to those of P(lambda) via constant uniform shifts, and eigenvectors and minimal bases of P(lambda) can be recovered from those of any of the strong block minimal bases tifications. In the special case where l divides d, we introduce a subfamily of strong block minimal bases matrix polynomials named "block Kronecker matrix polynomials", which is shown to be a fruitful source of companion t-ifications. (C) 2018 Elsevier Inc. All rights reserved.

- matrix polynomial; minimal indices; dual minimal bases; linearization; quadratification; strong l-ification; companion l-ification; dual minimal bases matrix polynomial; block kronecker matrix polynomial; fiedler companion linearizations; structured strong linearizations; vector-spaces; krylov methods; pencils; computation; framework; recovery