Bessel inequality strictly less

In summary, during the problem session, the conversation was about finding an example of an x in the sequence space \ell^2 that violates the Bessel inequality. The participants discussed various approaches and eventually came up with a simple solution. It was suggested to remove two elements from the orthonormal basis and the resulting sequence still satisfied the orthonormality condition but was no longer a basis. Thus, the sum of squared inner products was equal to 0 and the norm of x was still 2. This example successfully demonstrated a strict inequality in the Bessel inequality.
  • #1
jgthb
8
0
Hi everyone

Today during problem session we had this seemingly simple exercise, but I just can't crack it:

We should give an example of an [tex]x \in \ell^2[/tex] with strict inequality in the Bessel inequality (that is an x for which [tex]\sum_{k=1}^\infty |<x,x_k>|^2 < ||x||^2[/tex], where [tex](x_k)[/tex] is an orthonormal basis). I have tried a few things, e.g. defining [tex]x_k[/tex] in the following way:

[tex]x_1 = (\frac{1}{\sqrt(2)},\frac{1}{\sqrt(2)},0,\ldots)[/tex]
[tex]x_2 = (\frac{-1}{\sqrt(2)},\frac{1}{\sqrt(2)},0,\ldots)[/tex]
[tex]x_k = (0,\ldots,0,1,0,\ldots)[/tex], for [tex]k \geq 3[/tex]

and defining x as [tex]x = (1,1,0,0,\ldots)[/tex],

but that doesn't seem to work. Does anyone have a better idea?
 
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  • #2
jgthb said:
Bessel inequality (that is an x for which

[tex]\sum_{k=1}^\infty |<x,x_k>|^2 < ||x||^2,[/tex]

where [tex](x_k)[/tex] is an orthonormal basis).
Bessel's inequality concerns an orthonormal set (sequence). If that set is a basis, then equality holds: 'Parseval's Identity'.

That said, why wouldn't your example work? You have ||x||^2=2 and that sum of squared inner products equals 2/sqrt(2).
 
  • #3
set of course, not basis...thank you

but it doesn't work:

[tex]\sum_{k=1}^\infty |<x,x_k>|^2 =|<x,x_1>|^2=|\frac{2}{\sqrt(2)}|^2=2[/tex],

or am I just really bad at simple calculations today?
 
  • #4
Yes, you are right; I forgot to square. But this should come as no surprise, because your x_k's form a basis! Why don't you just throw x_1 and x_2 away? What you're left with is of course still orthonormal:
(0,0,1,0,0,0,...)
(0,0,0,1,0,0,...)
(0,0,0,0,1,0,...)
etc., but not a basis any more.

Then the sum of square of innner products is just 0, and ||x||^2=2. Certainly, 0<2 :)
 
  • #5
of course! that's actually a bit embarrassing, that I didn't think of that :)

Thank you so much, Landau
 
  • #6
Your're welcome :)
 

Related to Bessel inequality strictly less

1. What is the Bessel inequality strictly less?

The Bessel inequality strictly less is a mathematical concept that states that the sum of the squares of a set of numbers is always less than or equal to the sum of the squares of their corresponding Bessel coefficients. This inequality is often used in the analysis of signals and systems.

2. How is the Bessel inequality strictly less used in scientific research?

The Bessel inequality strictly less is commonly used in various fields of science, particularly in signal processing and engineering, to determine the accuracy and stability of systems. It is also used in statistics to measure the closeness of a set of data to a given model.

3. Can the Bessel inequality strictly less be proven?

Yes, the Bessel inequality strictly less can be proven using mathematical methods. The proof involves using the Cauchy-Schwarz inequality and the properties of Bessel functions.

4. How does the Bessel inequality strictly less relate to other mathematical concepts?

The Bessel inequality strictly less is closely related to other mathematical concepts such as the Cauchy-Schwarz inequality, the Parseval's theorem, and the Plancherel's theorem. It is also related to the concept of orthogonality in vector spaces.

5. Are there any practical applications of the Bessel inequality strictly less?

Yes, the Bessel inequality strictly less has many practical applications, particularly in the fields of engineering, physics, and statistics. It is used to analyze signals and systems, determine the accuracy of measurements, and in the analysis of data sets.

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