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I have a set of signals $S_1,\dots,S_N$, for which I am numerically computing the Fourier transforms $F_1,\dots,F_N$. Where $S_i$'s and $F_i$'s are C++ vectors (my computations are in C++).

My Computation objective is as follows:

1> compute product: $F = F_1 \times \dots \times F_N$

2> inverse Fourier transform F to get a S.

What I numerically observe is when I am trying to compute the product either I am running into situation where numbers become too small. Or numbers become too big.

I thought of scaling the vector $F_1$ by say a scalar number $a_1$ so that the overflow or underflow is avoided.

With the scaling my step 1 above will become

$F' = (\frac{F_1}{a_1})\times \dots \times(\frac{F_N}{a_N}) = F'_1\times \dots \times F'_N$

And when I inverse transform my final S' will differ from S by a scale factor. The structural form of the S will not change. By this I mean only the normalization of S is different.

My question is :

1> Is my rationale correct.

2> If my rationale is correct then given a C++ vector $F_i$ how can I choose a good scale $a_i$ to scale up or down the $F_i$'s.

Many thanks in advance.

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  • $\begingroup$ Use $\frac{a}{b}$ for $\frac{a}{b}$, $\times$ for $\times$, and $\dots$ for $\dots $. $\endgroup$ – Shaun Oct 5 '17 at 13:52
  • $\begingroup$ The computational science SE is probably a more appropriate place for this question. $\endgroup$ – Stelios Oct 5 '17 at 15:21
  • $\begingroup$ Are you using fixed precision? Or you are overflowing with double-precision? $\endgroup$ – AnonSubmitter85 Oct 8 '17 at 19:13
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In general, the Fourier transform of a product is equal to the convolution of the Fourier transforms of the factors.

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