Suppose I have a multivariate function $f$ from $\mathbb{C}^d$ to $\mathbb{C}$ that accepts a Taylor expension of the form
$$f(\mathbf x) = \sum\limits_{\mathbf k \in \mathbb N^d} a_{\mathbf k} \mathbf x^\mathbf k.$$
I do have a closed form expression for this function (which is a little complex to expose), allowing me to extract some information: e.g. I know that $f$ is $\mathcal{C}^{\infty}$, but I could extract more info if needed.
I want to bound the remainder of the approximation, by showing that $f$ belongs to some smooth functional ball like:
$$B_1(s,L) = \left\{ f: \sum\limits_{\mathbf k \in \mathbb N^d} a_{\mathbf k}^2 \mathbf k^s \le L\right\} \text{for positive $s,L$}$$
or:
$$B_2(r,L) = \left\{ f: \sum\limits_{\mathbf k \in \mathbb N^d} a_{\mathbf k}^2 e^{<\mathbf r,\mathbf k>} \le L\right\} \text{for positive $r,L$}$$
Question 1: Have these balls names? Are they known things, and is there some theory about them?
Question 2: What would it mean for $f$ to belong to one of these balls? What property of $f$ is necessary? Sufficient? Both?
Question 3: More directly, the quantity I really need to bound is, for a given $p \in \mathbb{N}^d$, the error $E_{\mathbf p} = \sum\limits_{\mathbf k \ge \mathbf p} a_{\mathbf k}^2$. Is there a way to bound this quantity from information about $f$ ?
[Please retag if not tagged correctly]