  In many applications, linear systems arise where the coefficient matrix takes
the special form ${\bf I} + {\bf K} + {\bf E}$, where ${\bf I}$ is the identity
matrix of dimension $n$, ${\rm rank}({\bf K}) = p \ll n$, and $\|{\bf E}\| \leq
\epsilon < 1$. GMRES convergence rates for linear systems with coefficient
matrices of the forms ${\bf I} + {\bf K}$ and ${\bf I} + {\bf E}$ are
guaranteed by well-known theory, but only relatively weak convergence bounds
specific to matrices of the form ${\bf I} + {\bf K} + {\bf E}$ currently exist.
In this paper, we explore the convergence properties of linear systems with
such coefficient matrices by considering the pseudospectrum of ${\bf I} + {\bf
K}$. We derive a bound for the GMRES residual in terms of $\epsilon$ when
approximately solving the linear system $({\bf I} + {\bf K} + {\bf E}){\bf x} =
{\bf b}$ and identify the eigenvalues of ${\bf I} + {\bf K}$ that are sensitive
to perturbation. In particular, while a clustered spectrum away from the origin
is often a good indicator of fast GMRES convergence, that convergence may be
slow when some of those eigenvalues are ill-conditioned. We show there can be
at most $2p$ eigenvalues of ${\bf I} + {\bf K}$ that are sensitive to small
perturbations. We present numerical results when using GMRES to solve a
sequence of linear systems of the form $({\bf I} + {\bf K}_j + {\bf E}_j){\bf
x}_j = {\bf b}_j$ that arise from the application of Broyden's method to solve
a nonlinear partial differential equation.
