Block Kronecker linearizations of matrix polynomials and their backward errors

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We introduce a new family of strong linearizations of matrix polynomialswhich we call block Kronecker pencilsand perform a backward stability analysis of complete polynomial eigenproblems. These problems are solved by applying any backward stable algorithm to a block Kronecker pencil, such as the staircase algorithm for singular pencils or the QZ algorithm for regular pencils. This stability analysis allows us to identify those block Kronecker pencils that yield a computed complete eigenstructure which is exactly that of a slightly perturbed matrix polynomial. The global backward error analysis in this work presents for the first time the following key properties: it is a rigorous analysis valid for finite perturbations (i.e., it is not a first order analysis), it provides precise bounds, it is valid simultaneously for a large class of linearizations, and it establishes a framework that may be generalized to other classes of linearizations. These features are related to the fact that block Kronecker pencils are a particular case of the new family of strong block minimal bases pencils, which are robust under certain perturbations and, so, include certain perturbations of block Kronecker pencils.
Backward error analysis, Polynomial eigenvalue problems, Complete eigenstructure, Dual minimal bases, Linearization, Matrix polynomials, Matrix perturbation theory, Minimal indices
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Dopico, F. M., Lawrence, P. W., Pérez, J., Dooren, P. V. (2018). Block Kronecker linearizations of matrix polynomials and their backward errors. Numerische Mathematik, 140(2), pp. 373–426.