I am reading a passage from Perko's book about the Stable Manifold Theorem. Here is the problem:
Let $\dot x = f(x)$ be a system where $f: E \subset \mathbb{R}^{n} \rightarrow \mathbb{R}^n$ (with $E$ open and containing the origin). The linearization of this system at $0$ is $\dot x = Ax$ where $A = Df(0)$. Suppose $A$ has $k$ eigenvalues $\lambda_{j}$ with negative real part and $n - k$ eigenvalues $\lambda_{j}$ with positive real part. Then we can write the eigendecomposition as $A = C\Lambda C^{-1}$ with
$\Lambda = \begin{bmatrix} P & 0 \\ 0 & Q \end{bmatrix}$, where $P$ is the matrix for the first $k$ eigenvalues and $Q$ is the matrix for the remaining $n - k$ eigenvalues. For the eigenvalues in $P$, choose $\alpha > 0$ sufficiently small so that $Re(\lambda_{j}) < -\alpha < 0$.
If we define $U(t) = \begin{bmatrix} e^{Pt} & 0 \\ 0 & 0 \end{bmatrix}$ and $V(t) = \begin{bmatrix} 0 & 0 \\ 0 & e^{Qt} \end{bmatrix}$, then $\dot U = \Lambda U$ and $\dot V = \Lambda V$.
Now we get to something that Perko claims but does not prove: There exists $K > 0$ sufficiently large and $\sigma > 0$ sufficiently small that
$\|U(t)\| \leq Ke^{-(\alpha + \sigma)t}$ for $t \geq 0$ and $\|V(t)\| \leq Ke^{\sigma t}$ for $t \leq 0$.
There is a discrete version of the first part discussed here, but that problem only addresses a matrix $A$ with no unstable subspace. Can someone explain how to prove the full claim here? Thanks.