Recovering a function from limited info (Fourier transforms)

In summary, the question is whether we can recover an unknown function f(x) from its squared absolute values and the squared absolute values of its Fourier transform. However, counterexamples exist where two different functions give the same results, so the question is modified to ask if we can recover f(x) up to an overall constant factor of the form e^(iφ).
  • #1
pellman
684
5

Homework Statement


Consider some unknown function f:R --> C. Denote its Fourier transform by F. Suppose we know |f(x)|2 for all x and |F(k)|2 for all k. Can we recover f(x) (for all x) from this information?

Homework Equations


None.

The Attempt at a Solution


None. It's a yes or no question. Please just point me to the theorem if you know it. Thanks!

<Mentor note: approved.>
 
Last edited:
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  • #2
Counter example: ##f(x) = \exp(-t^2/2)## and ##f(x) = i \exp(-t^2/2)## give the same results for ##|f(x)|^2## and ##|F(k)|^2##.
 
  • #3
DrClaude said:
Counter example: ##f(x) = \exp(-t^2/2)## and ##f(x) = i \exp(-t^2/2)## give the same results for ##|f(x)|^2## and ##|F(k)|^2##.
Thanks! So now I need to modify the question. Can we recover f(x) up to an overall constant factor of the form ##e^{i \phi}## ? The origin of the question is in quantum theory where such a constant factor has no physical significance.
 

Related to Recovering a function from limited info (Fourier transforms)

1. How are Fourier transforms used to recover a function from limited information?

Fourier transforms are mathematical operations that decompose a function into its individual frequency components. By analyzing the frequency content of a signal, it is possible to reconstruct the original function from limited information, such as a limited number of data points or a noisy signal.

2. What is the relationship between the time domain and frequency domain in Fourier transforms?

In Fourier transforms, the time domain represents the original function while the frequency domain represents the frequency components of the function. This allows us to analyze the function in both the time and frequency domains, providing valuable information about the behavior of the function.

3. Are there any limitations to using Fourier transforms for function recovery?

While Fourier transforms are a powerful tool for recovering functions, there are some limitations. For example, if the function contains sharp discontinuities or infinite slopes, it may not be accurately represented in the frequency domain. Additionally, the accuracy of the reconstruction depends on the amount and quality of the limited information available.

4. How does the number of data points affect the accuracy of function recovery using Fourier transforms?

The more data points that are available, the more accurate the reconstruction of the function will be. As the number of data points approaches infinity, the reconstructed function will be an exact representation of the original function.

5. Can Fourier transforms be used for any type of function recovery?

Fourier transforms can be used for a wide range of function recovery applications, but they are most commonly used for periodic and continuous functions. For functions that are not periodic or continuous, other mathematical tools may be more appropriate.

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