Large vector spaces of block-symmetric strong linearizations of matrix polynomials Articles uri icon

publication date

  • July 2015

start page

  • 165

end page

  • 210


  • July


  • 477

International Standard Serial Number (ISSN)

  • 0024-3795

Electronic International Standard Serial Number (EISSN)

  • 1873-1856


  • Given a matrix polynomial P(lambda) = Sigma(k)(i=0) lambda(i) A(i) of degree k, where A(i) are n x n matrices with entries in a field F, the development of linearizations of P(A) that preserve whatever structure P(lambda) might posses has been a very active area of research in the last decade. Most of the structure-preserving linearizations of P(lambda) discovered so far are based on certain modifications of block-symmetric linearizations. The block-symmetric linearizations of P(lambda) available in the literature fall essentially into two classes: linearizations based on the so-called Fiedler pencils with repetition, which form a finite family, and a vector space of dimension k of block-symmetric pencils, called DL(P), such that most of its pencils are linearizations. One drawback of the pencils in DL(P) is that none of them is a linearization when P(lambda) is singular. In this paper we introduce new vector spaces of block,symmetric pencils, most of which are strong linearizations of P(A). The dimensions of these spaces are O(n(2)), which, for n >= root k, are much larger than the dimension of DL(P). When k is odd, many of these vector spaces contain linearizations also when P(lambda) is singular. The coefficients of the block-symmetric pencils in these new spaces can be easily constructed as k x k block-matrices whose n x n blocks are of the form 0, +/-alpha I-n, +/-alpha A(i), or arbitrary n x n matrices, where a is an arbitrary nonzero scalar. (C) 2015 Elsevier Inc. All rights reserved.


  • block-symmetric linearizations; fiedler pencils with repetition; generalized fiedler pencils with repetition; matrix polynomials; strong linearizations; structured matrix polynomials; vector space dl(p)