Proposition 18.4.4 (Power Rule). Let $E$ be a topological vector space, $\sigma \subset B(E)$ be an upward-directed family that includes bounded sets contained in finite-dimensional subspaces, $F$ be a Hausdorff locally convex space, and

\[T \in \underbrace{L(E; L(E; \cdots L(E; F) \cdots ))}_{n \text{ times}}\subset B^{n}(E; F)\]

be symmetric. For any $x \in E$ and $1 \le k \le n$, let $x^{(k)}$ denote the tuple of $x$ repeated $k$ times, then:

  1. The mapping $f: E \to F \quad x \mapsto T(x^{(n)})$ is infinitely $\sigma$-differentiable on $E$.

  2. For each $1 \le k \le n$ and $x, h \in E$,

    \[Df(x)(h_{1}, \cdots, h_{k}) = \frac{n!}{(n-k)!}T(x^{(n-k)}, h_{1}, \cdots, h_{k})\]

    In particular, $D^{k}f = n! \cdot T$.

  3. For each $k > n$ and $x \in E$, $Df(x) = 0$.

Proof. Suppose inductively that (2) holds for $0 \le k \le n$. Let $G = B^{k}_{\sigma}(E; F)$, then $D^{k}_{\sigma} f \in B^{n-k}_{\sigma}(E; G)$ under the identification $B^{n}_{\sigma}(E; F) = B^{n-k}_{\sigma}(E; B^{k}_{\sigma}(E; F))$ in Proposition 8.11.3. By Theorem 18.4.3, $D^{k}_{\sigma} f$ is also symmetric, so using the Binomial formula,

\begin{align*}D^{k}_{\sigma} f(x + h)&= \sum_{r = 0}^{n-k}{n - k \choose r}D^{k}_{\sigma} f(x^{(n-k-r)}, h^{(r)}) \\&= f(x) + (n-k)D^{k}_{\sigma} f(x^{(n-k-1)}, h) \\&+ \underbrace{\sum_{r = 2}^{n-k}{n - k \choose r}D^k_\sigma f(x^{(n-k-r)}, h^{(r)})}_{r(h)}\end{align*}

For each $k \ge 2$, let $A \in \sigma$ and $U \in \cn_{F}(0)$, then since $D^{k}_{\sigma} f \in B^{n-k}_{\sigma}(E; F)$, there exists $t > 0$ such that

\[\frac{D^{k}_{\sigma} f(x^{(n-k)}, (sA)^{(k)})}{t}= s^{k-1}D^{k}_{\sigma} f(x^{(n-k)}, A^{(k)}) \subset U\]

for all $s \in (0, t)$. Hence $r \in \mathcal{R}_{\sigma}(E; G)$, and

\[D^{k+1}_{\sigma} f(x + h) = f(x) + \frac{n!}{(n-k-1)!}T(x^{(n-k-1)}, h_{1}, \cdots, h_{k+1}) + r(h)\]

by the inductive hypothesis.

(3): Since $D^{n}_{\sigma} f$ is constant, $D^{k}_{\sigma} f = 0$ for all $k > n$.$\square$