Inner product in n-dimensional vector space

In summary, gucci, the problem is to prove that the inner product of a and b equals 1/4[|a+b|^2 - |a-b|^2] (a, b in C^n or an n-dimensional vector space with complex elements).
  • #1
gucci1
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So, I have an equivalence I need to prove, but I think I'm having trouble understanding the problem at a basic level.

The problem is to prove that the inner product of a and b equals 1/4[|a+b|^2 - |a-b|^2] (a, b in C^n or an n-dimensional vector space with complex elements).

I don't understand how to write out |a+b|^2 in other terms. If anyone has any guidance here, that would be awesome. :-/
 
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  • #2
gucci said:
So, I have an equivalence I need to prove, but I think I'm having trouble understanding the problem at a basic level.

The problem is to prove that the inner product of a and b equals 1/4[|a+b|^2 - |a-b|^2] (a, b in C^n or an n-dimensional vector space with complex elements).

I don't understand how to write out |a+b|^2 in other terms. If anyone has any guidance here, that would be awesome. :-/

Welcome to MHB, gucci! :)

By definition the norm is given by $|x|^2 = \langle x, x \rangle$.

So:
$$|a+b|^2 = \langle a+b, a+b \rangle = \langle a, a+b \rangle + \langle b, a+b \rangle = ...$$

Can you continue using the axioms of an inner product?
 
  • #3
Since you are working with a complex inner product, it might be helpful to know that:

\(\displaystyle \langle a,a+b \rangle = \overline{\langle a+b,a \rangle} = \overline{\langle a,a \rangle + \langle b,a \rangle}= \overline{\langle a,a\rangle} + \overline{\langle b,a\rangle} = \langle a,a\rangle + \langle a,b\rangle\)

and that:

\(\displaystyle \langle a,a-b\rangle = \langle a,a+(-b)\rangle = \langle a,a\rangle + \langle a,-b\rangle = \langle a,a\rangle + (\overline{-1})\langle a,b\rangle = \langle a,a\rangle - \langle a,b\rangle\)
 
  • #4
So, with your help I am much closer to the answer, but I'm making a mistake somewhere I guess. This is what I'm coming up with:

1/4 [|a+b|^2 - |a-b|^2] = 1/4 [⟨a+b,a+b⟩ - ⟨a+(-1)b,a+(-1)b⟩]
= 1/4 [⟨a,a+b⟩ + ⟨b,a+b⟩ - ⟨a,a+(-1)b⟩ + ⟨b,a+(-1)b⟩]
= 1/4 [⟨a,a⟩ + ⟨a,b⟩ + ⟨b,a⟩ + ⟨b,b⟩ - ⟨a,a⟩ + ⟨a,b⟩ + ⟨b,a⟩ - ⟨b,b⟩]
= 1/4 [2⟨a,b⟩ + 2⟨b,a⟩]

Can anyone spot where I slipped up? Thanks for all your help
 
  • #5
gucci said:
So, with your help I am much closer to the answer, but I'm making a mistake somewhere I guess. This is what I'm coming up with:

1/4 [|a+b|^2 - |a-b|^2] = 1/4 [⟨a+b,a+b⟩ - ⟨a+(-1)b,a+(-1)b⟩]
= 1/4 [⟨a,a+b⟩ + ⟨b,a+b⟩ - ⟨a,a+(-1)b⟩ + ⟨b,a+(-1)b⟩]
= 1/4 [⟨a,a⟩ + ⟨a,b⟩ + ⟨b,a⟩ + ⟨b,b⟩ - ⟨a,a⟩ + ⟨a,b⟩ + ⟨b,a⟩ - ⟨b,b⟩]
= 1/4 [2⟨a,b⟩ + 2⟨b,a⟩]

Can anyone spot where I slipped up? Thanks for all your help

I don't think you have made a mistake, in a complex inner-product space what you have is:

\(\displaystyle \frac{1}{4}(2\langle a,b\rangle + 2\langle b,a\rangle) = \frac{1}{2}(\langle a,b\rangle + \overline{\langle a,b\rangle}) = \mathfrak{Re}(\langle a,b\rangle)\)

What you are being asked to PROVE is incorrect, in a complex inner product space the polarization identity is actually:

\(\displaystyle \langle a,b\rangle = \frac{|a+b|^2 - |a-b|^2 + i|a+ib|^2 - i|a-ib|^2}{4}\)
 
  • #6
Thank you so much! I love this forum, everyone here is so helpful :D
 

Related to Inner product in n-dimensional vector space

1. What is an inner product in n-dimensional vector space?

An inner product in n-dimensional vector space is a mathematical operation that takes in two vectors and produces a scalar value. It is typically denoted as <x, y> and has properties such as linearity, symmetry, and positive definiteness.

2. How is the inner product different from the dot product?

The inner product is a generalization of the dot product, which is only defined for vectors in three-dimensional space. The inner product can be defined for vectors in any number of dimensions, making it a more versatile and powerful tool in mathematics and physics.

3. What are some applications of the inner product in n-dimensional vector space?

The inner product is used in various fields such as linear algebra, functional analysis, quantum mechanics, and signal processing. It is also commonly used in machine learning algorithms for tasks like dimensionality reduction and clustering.

4. How is the inner product calculated in n-dimensional vector space?

The inner product is calculated by taking the dot product of the two vectors and multiplying it by the cosine of the angle between them. In n-dimensional space, this can be represented using the summation of the product of the corresponding components of the two vectors.

5. What is the geometric interpretation of the inner product in n-dimensional vector space?

The inner product can be interpreted geometrically as the projection of one vector onto another, scaled by the length of the second vector. It also measures the angle between two vectors and can be used to determine whether they are orthogonal (perpendicular) or not.

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