Approximation of functions by Gaussians

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Any vector in L^2(R^3) can be approximated by a finite sum of Gaussian vectors, as stated in the referenced paper. This claim suggests that Gaussians form an overcomplete basis for square integrable functions, which may extend to higher dimensions. The concept of Gaussian functions relates to coherent states in the context of harmonic oscillators. There is substantial literature discussing the applications and generalizations of these coherent states as an over-complete basis. Understanding this approximation could be beneficial for various mathematical and physical applications.
Orbb
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Hey everyone,

in doi:10.1016/0375-9601(82)90182-7 I found the following claim:

"Any vector of L^2(\mathbb{R}^3) can be arbitrarily well approximated by a finite sum of gaussian vectors."

Is this actually true? I lack the insight on how to prove this, but it would be a useful argument I could use in some other context. If true, I guess it would also generalize to arbitrary dimensions? Thanks in advance for any insights.

Edit: Okay, from what I've read in some papers, it seems the Gaussians form an overcomplete basis in the space of square integrable functions.
 
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Hi Orbb,

It seems like you've already worked it out, but just for completeness...
For the harmonic oscillator, the Gaussian functions are called "coherent states". There's quite a large literature on them (and their various generalisations) and their uses as an over-complete basis.

Simon
 
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