Bessel's Inequality: Proving \|v\|^2 \ge c_1^2 + \cdots + c_k^2

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In summary, the conversation discussed how to prove an inequality involving the projection coefficients of a vector v onto an orthonormal subset. It was determined that completing the subset to an orthonormal basis and using Gram-Schmidt would lead to an orthonormal basis with the original subset. Two proofs were requested: Parseval's Identity and Bessel's Inequality. Parseval's Identity states that the norm squared of a vector w in the span of an orthonormal subset is equal to the sum of the squared inner products between w and each element of the subset. Bessel's Inequality states that the norm squared of any vector x is greater than or equal to the sum of the squared inner products between x and each element of the
  • #1
e(ho0n3
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Homework Statement
Let [itex]v \in \mathbb{R}^n[/itex], let [itex]\{u_1, \ldots, u_k\}[/itex] be an orthonormal subset of [itex]\mathbb{R}^n[/itex] and let [itex]c_i[/itex] be the coefficient of the projection of v to the span of [itex]u_i[/itex]. Show that [itex]\|v\|^2 \ge c_1^2 + \cdots + c_k^2[/itex].

The attempt at a solution
[itex]c_i = v \cdot u_i[/itex] and [itex]\| v \|^2 = v \cdot v[/itex] so I can write the inequality as

[tex]v \cdot (v - (u_1 + \cdots + u_k)) \ge 0[/tex]

This means the angle between v and [itex]v - (u_1 + \cdots + u_k)[/itex] is less than 90 degrees. This is all I've been able to conjure. I'm trying to reverse-engineer the inequality back to something I know is true. Is this a good approach? Is there a better approach?
 
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  • #2
Couldn't you just complete {u1,...,uk} to an orthonormal basis {u1,...,uk,uk+1,...,un}? So ||v||^2=c1^2+...cn^2.
 
  • #3
Ah! Good point. I think that'll work. All that remains to be proved is that [itex]v = c_1u_1 + \cdots c_nu_n[/itex].
 
  • #4
Quick question: the basis doesn't have to be orthonormal does it?
 
  • #5
e(ho0n3 said:
Ah! Good point. I think that'll work. All that remains to be proved is that [itex]v = c_1u_1 + \cdots c_nu_n[/itex].

Do you really have to prove that? v is some linear combination of the basis vectors u_i. So c_i must be v.u_i since they are orthonormal. Isn't that what an orthonormal basis is all about?
 
  • #6
Never mind. I understand why it has to be orthonormal: If it isn't, I couldn't write v as a linear combination of the u's using the c's as the coefficients.
 
  • #7
The only thing that might have to be proved is that you can complete an orthonormal subset to an orthonormal basis. But that's Gram-Schmidt.
 
  • #8
I know I can expand the set of u's to a basis, then orthogonalize it using Gram-Schmidt, and then normalize the result. This will yield an orthonormal basis with the original u's.
 
  • #9
Right. So not much to prove really. That one was easy.
 
  • #10
Thank you for your help.
 
  • #11
Hi
Can you do it these two proof?
I tried but i don't know these proofs...

(b) Prove Parseval’s Indentity: For any w ∈ span(S), we have

||w||^2 = |w · u1 |^2 + |w · u2 |^2+ · · · + |w · uk |^2 .

(c) Prove Bessel’s Inequality: For any x ∈ R^n we have

||x||^2 ≥ |x · u1 |^2 + |x · u2 |^2 + · · · + |x · uk |^2 ,

and this is an equality if and only if x ∈ span(S).
 

1. What is Bessel's inequality and how is it used?

Bessel's inequality is a mathematical theorem that states that the sum of the squares of the coefficients in an orthonormal basis is always less than or equal to the norm squared of a vector in a vector space. It is used in various fields of mathematics, including linear algebra and functional analysis.

2. What is the significance of \|v\|^2 \ge c_1^2 + \cdots + c_k^2?

This inequality is significant because it allows for the measurement of the length of a vector in a vector space. It also plays an important role in proving other theorems and properties in mathematics.

3. How is Bessel's inequality proved?

Bessel's inequality can be proved using mathematical induction, where the base case involves proving the inequality for a vector with only one component, and the inductive step involves using the orthonormality of the basis to show that the inequality holds for a vector with k+1 components.

4. Can Bessel's inequality be generalized to other vector spaces?

Yes, Bessel's inequality can be generalized to any inner product space. However, the basis of the vector space must be orthonormal for the inequality to hold.

5. How is Bessel's inequality related to other mathematical concepts?

Bessel's inequality is closely related to other concepts in mathematics, such as Parseval's identity, which states that the sum of the squares of the coefficients in an orthonormal basis is equal to the norm squared of the vector. It is also related to the Cauchy-Schwarz inequality, which states that the inner product of two vectors is always less than or equal to the product of their norms.

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