Extending an Isometry in Schwartz Space to Moderately Decreasing Functions

In summary, we are trying to show that there exists a unique extension of the Fourier transform, F, from the Schwartz space, S(R), to the set of moderately decreasing functions, M(R), which is also an isometry. This means that the norm of F(g) is equal to the norm of g for every g in S(R). To prove this, we can use the fact that for any function g in M(R), there exists a sequence {g_n} in S(R) such that the norm of g_n - g converges to 0. We can also show that F is 1-1, onto, and linear, and use the hint to show that there exists F(h_k) converging to F(f
  • #1
creepypasta13
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Homework Statement



let S(R) be the schwartz space, M(R) be the set of moderately decreasing functions, F be the Fourier transform

Suppose F:S(R)->S(R) is an isometry, ie is satisfies ||F(g)|| = ||g|| for every g in S(R). Show that there exists a unique extension G: M(R)->M(R) which is an isometry, ie a function G: M(R)->M(R) so that for any g in S(R) we have G(g) = F(g), and for any g in M(R) we have ||G(g)|| = ||g||.

hint: You may use that for any g in M(R) there exists a sequence {g_n} subset in S(R) such that ||g_n - g|| converges to 0

note: make sure you prove that both that G exists, and that it is unique

Homework Equations





The Attempt at a Solution



i was thinking of showing that F is 1-1, onto, and linear, just to expand my options. also using the hint to show that there exists F(h_k) converging to F(f), and then ||h_k - f|| converges to 0, and then defining h_k(t) as equal to f(t) for all |t| < k, and 0 otherwise
 
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  • #2
i heard that to do it for the L2, you extend it by density, but i don't know what that means
 

FAQ: Extending an Isometry in Schwartz Space to Moderately Decreasing Functions

What is Fourier analysis?

Fourier analysis is a mathematical technique used to decompose a complex function into simpler trigonometric functions. It is commonly used to analyze and represent periodic or non-periodic signals in fields such as engineering, physics, and mathematics.

What is the purpose of Fourier analysis?

The purpose of Fourier analysis is to break down a complex signal into its basic components, making it easier to understand and manipulate. It allows us to identify the frequencies present in a signal and their relative strengths, which can be useful in applications such as filtering, compression, and data analysis.

How is Fourier analysis performed?

Fourier analysis is typically performed using mathematical algorithms, such as the Fast Fourier Transform (FFT). These algorithms use complex mathematical operations to convert a signal from the time domain (amplitude vs. time) to the frequency domain (amplitude vs. frequency).

What are the limitations of Fourier analysis?

Fourier analysis assumes that a signal is composed of simple sine and cosine waves, which may not always be accurate in real-world scenarios. It also assumes that the signal is periodic, which may not be true for non-repetitive signals. Additionally, Fourier analysis does not take into account the time or spatial relationships between different components of a signal.

What are the applications of Fourier analysis?

Fourier analysis has many practical applications, including signal processing, image and sound compression, data compression, and image reconstruction. It is also used in fields such as astronomy, geology, and medical imaging to analyze and interpret complex signals and data.

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