A framework for analyzing nonlinear eigenproblems and parametrized linear systems

Associated with an n x n matrix polynomial of degree l, P(lambda) = Sigma(l)(j=0) lambda(j)A(j). are the eigenvalue problem P(lambda)x = 0 and the linear system problem P(omega)x = b, where in the latter case x is to be computed for many values of the parameter omega. Both problems can be solved by conversion to an equivalent problem L(lambda)z = 0 or L(omega)z = c that is linear in the parameter lambda or omega. This linearization process has received much attention in recent years for the eigenvalue problem, but it is less well understood for the linear system problem. We develop a framework in which more general versions of both problems can be analyzed, based on one-sided factorizations connecting a general nonlinear matrix function N(lambda) to a simpler function M(lambda), typically a polynomial of degree 1 or 2. Our analysis relates the solutions of the original and lower degree problems and in the linear system case indicates how to choose the right-hand side c and recover the solution x from z. For the eigenvalue problem this framework includes many special cases studied in the literature, including the vector spaces of pencils L-1(P) and L-2(P) recently introduced by Mackey, Mackey, Mehl, and Mehrmann and a class of rational problems. We use the framework to investigate the conditioning and stability of the parametrized linear system P(omega)x = b and thereby study the effect of scaling, both of the original polynomial and of the pencil L. Our results identify situations in which scaling can potentially greatly improve the conditioning and stability and our numerical results show that dramatic improvements can be achieved in practice. (C) 2009 Elsevier Inc. All rights reserved