Main Question

I have a class of high-dimensional functions which obey certain constraints (of value or partial derivative) on specific submanifolds of their domain. I am quite confident that there is a more specific class of functions that matches these functions on these submanifolds and approximates them in the vicinity. I want to prove this or find out what additional assumptions I need to make. To this end, I am looking for a mathematical subfield or formalism that deals with such questions.


A simple statement along the lines of what I wish to prove is:

If $f(0,y) = f(x,0) = 0$ and $f$ is sufficiently benign, there exist functions $g$, $h$, and $r$ such that:

  • $f(x,y) = g(x) h(y) + r(x,y)$,
  • $g(0) = h(0) = 0$,
  • $r(x,0) = r(0,y) = 0$,
  • $∂_1 r(0,y) = ∂_2 r(x,0) = 0$.

In this case, the answer is to choose $g(x) = \frac{∂_2 f(x,0)}{∂_1 ∂_2 f(0,0)}$, $h(x) = ∂_1 f(0,y)$, and $r(x,y) = f(x,y) - g(x) h(y)$.

What I considered so far

Taylor series allow me to perform pointwise function approximations. If done sufficiently general, this may allow me to find the shape of the desired approximation, but the process is rather tedious and suggests that there is a more general variant of it.

Approximation theory seems to focus on numerically finding best approximations on the entire domain without paying special attention to certain regions – which is not what I want.

I already asked this question two weeks ago on Math SE and placed a bounty on it without success so far.


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