Do Fourier Coefficients Decay with Increasing n?

You can make the statement correct by replacing "and" with "or" and by using the fact that a1^2+b1^2<=M (from Parseval's identity) to find an upper bound for |a1| and |b1|.
  • #1
s_j_sawyer
21
0

Homework Statement



Let f be a C1 function on [-pi,pi]. Prove the Fourier coefficients of f satisfy

|an| <= K/n and |bn| <= L/n n=1,2,...

Homework Equations



an = 1/pi * int[-pi..pi] (f(x)*cos(nx)) dx

bn = 1/pi * int[-pi..pi] (f(x)*sin(nx)) dx

Sorry if my form is slightly unpleasing to the eye, but I'm sure if you're reading my post you probably know what I'm talking about.

The Attempt at a Solution



|an| = | 1/pi * int[-pi..pi] (f(x)*cos(nx)) dx |

<= 1/pi * int[-pi..pi] | (f(x)*cos(nx)) | dx

and that's as far as I could get. I thought maybe I could show that

int[-pi..pi]( | cosnx | )dx <= (1/n)*int[-pi..pi]( |cosx| ) dx

but that turned out to be false.

Any ideas?
 
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  • #2
not there, but some ideas that are hopefully helpful
so I = [itex] \left[ -\pi, \pi \right] [/itex] is a closed interval

as the function is [itex] C^1 [/itex] both f & f' are continuous on I and so attain a maximum on I

[tex]f = a_0 + \sum_{n=1} (a_n cos(nx) + b_n sin(nx)) [/tex]

[tex]f' = \sum_{n=1} (-n.a_n sin(nx) + n .b_n cos(nx)) [/tex]

as f and f' are bounded, then applying Parseval's theorem, for some positive number M
[tex]\int f'^2 = M [/tex]
[tex]= \pi \left( \sum_{n=1} (n.a_n)^2 + (n .b_n)^2 \right) [/tex]
 
  • #3
Ok I followed everything you said but I still don't see how this relates to what I'm trying to show. The fact that there is a /n necessary is really confusing me.

i.e. |an| <= K/n and not just KEdit:

Ok I may have gotten it.

I think the solution is |an| <= |a1|/n and |bn| <= |b1|/n

There's too much to write out how I got this but does this seem correct?
 
Last edited:
  • #4
s_j_sawyer said:
Ok I followed everything you said but I still don't see how this relates to what I'm trying to show. The fact that there is a /n necessary is really confusing me.

i.e. |an| <= K/n and not just K
The 1/n factor is the reason lanedance looked at the series for f' rather than f. Look at the series from Parseval's identity. What can you say about the sign of each term?
Edit:

Ok I may have gotten it.

I think the solution is |an| <= |a1|/n and |bn| <= |b1|/n

There's too much to write out how I got this but does this seem correct?
No, this isn't correct. f(x)=sin(2x) is a simple counterexample since it has a1=0 and a2=1.
 

Related to Do Fourier Coefficients Decay with Increasing n?

What are Fourier coefficients?

Fourier coefficients are complex numbers that represent the amplitude and phase of a specific frequency component in a signal. They are used to break down a complicated signal into simpler components for analysis.

How are Fourier coefficients calculated?

The Fourier coefficients are calculated using a mathematical technique called Fourier analysis. This involves taking the original signal and decomposing it into a series of sine and cosine waves with different frequencies and amplitudes. The coefficients are then determined by comparing the original signal to these component waves.

What is the significance of the Fourier coefficients property?

The Fourier coefficients property states that the coefficients of the Fourier series converge to the original signal as the number of terms in the series increases. This means that by using a sufficient number of terms, we can accurately reconstruct any signal and analyze its frequency components.

What is the difference between Fourier coefficients and Fourier transform?

Fourier transform is a mathematical operation that takes a signal in the time domain and converts it into a signal in the frequency domain. This involves calculating the Fourier coefficients for all possible frequencies. The Fourier coefficients, on the other hand, are the specific values that represent the amplitude and phase of each frequency component in the signal.

How are Fourier coefficients used in real-world applications?

Fourier coefficients have many practical applications, such as signal processing, image and sound compression, and filtering. They are also used in fields such as physics, engineering, and finance to analyze and model complex signals and systems.

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