I am finishing my Ph.D. dissertation in engineering and I would like to show a simple proof. I am having troubles formalizing my ideas into a proof though. I think in a mathematics paper this concept is obvious, but I think a simple proof will go a long way in an engineering paper.
I need to show that a thin-plate spline RBF approximation: \begin{equation} \mathcal{P}_f(\boldsymbol{x}) = \sum_{k=1}^N c_k r_k^2 \log (r_k) + P\left(\boldsymbol{x}\right) \end{equation}
is real analytic, where $r_k=||\boldsymbol{x}-\boldsymbol{x_k}||$, $c_k$'s are constant, $\boldsymbol{x}_k$ are constant training nodes and $P\left(\boldsymbol{x}\right)$ is a polynomial in the coordinates of $\boldsymbol{x}$, e.g. for $\boldsymbol{x}\in\mathbb{R}^2$ , $P\left(\boldsymbol{x}\right)=d_0 + d_1x_1 +d_2x_2$, where the $d$'s are the coefficients and $x_1$ and $x_2$ are the coordinates of $\boldsymbol{x}$.
I know that the sums, products, and compositions of real analytic functions are real analytic , that the Logarithm is real analytic on $\left(0,\infty\right)$, and that any real polynomial is real analytic.
Would it be appropriate to do something along the following:
Theorem 1: $\mathcal{P}_f$ is a real analytic function on $\left(0,\infty\right)$
Axiom 1: the sums, products, and compositions of real analytic functions are real analytic
Axiom 2: the Logarithm is real analytic on $\left(0,\infty\right)$
Axiom 3: any real polynomial is real analytic.
...
It's not clear whether the $x_k$ are points in space or numbers. In the RBF stuff I've seen, polynomial $P$ is typically linear/affine in the coordinates of $x$. Can you clarify?
– John Hughes Dec 18 '13 at 22:25