Linear Algebra - Inner Product problem

In summary: But, that's not really helpful. What else? Well, I can also see that it's always true that ##(u+w, v) = (u+w, v) + (u, w)##. So, if I can show that this is always true, then I've proven that ##(u, u) = 0##.
  • #1
RikaWolf
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TL;DR Summary
Prove/Disprove - Inner Product topic
I need help to know if I'm on the right track:
Prove/Disprove the following:
Let u ∈ V . If (u, v) = 0 for every v ∈ V such that v ≠ u, then u = 0.
(V is a vector-space)
I think I need to disprove by using v = 0, however I'm not sure.
 
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  • #2
RikaWolf said:
Summary: Prove/Disprove - Inner Product topic

I need help to know if I'm on the right track:
Prove/Disprove the following:
Let u ∈ V . If (u, v) = 0 for every v ∈ V such that v ≠ u, then u = 0.
(V is a vector-space)
I think I need to disprove by using v = 0, however I'm not sure.
I assume you have a real, finite dimensional vector space here.

You are right, ##(u,0)=0## is always true for any ##u##, which makes me think there is a typo. The condition is probably ##(u,v)=0## for every ##v\neq 0##, then ##u=0##.

What does it mean, that ##(u,v)=0## for all ##v\neq 0## and what does it mean if you chose a basis and express ##u## according to this basis?
 
  • #3
fresh_42 said:
I assume you have a real, finite dimensional vector space here.

You are right, ##(u,0)=0## is always true for any ##u##, which makes me think there is a typo. The condition is probably ##(u,v)=0## for every ##v\neq 0##, then ##u=0##.

What does it mean, that ##(u,v)=0## for all ##v\neq 0## and what does it mean if you chose a basis and express ##u## according to this basis?
Well then, assuming v= {b1...bn}, u={a1...an} there could still be a (u,v)=0 when v= {-an...-a} and u isn't 0
 
  • #4
##(u,v)=0## means that ##u## and ##v## are perpendicular to each other. Now how can a vector ##u## be perpendicular to all the rest? Have you had the Gram Schmidt orthogonalization algorithm in your course, yet?
 
  • #5
RikaWolf said:
Summary: Prove/Disprove - Inner Product topic

I need help to know if I'm on the right track:
Prove/Disprove the following:
Let u ∈ V . If (u, v) = 0 for every v ∈ V such that v ≠ u, then u = 0.
(V is a vector-space)
I think I need to disprove by using v = 0, however I'm not sure.

Let me add to the above by explaining how I thought about this:

First, why are we not given that ##(u, u) = 0##? Well, one of the inner product axioms is that ##(u, u) = 0## iff ##u =0##.

Now, that gives me an idea of how to prove that ##u = 0##. If I could show that ##(u, u) = 0## then that would be enough.

So, how to show that ##(u, u) = 0##?

First, I can see an easy way that is a bit of a cheat. So, if I don't use that, what else can I do?

Well, there's the linearity of the inner product: ##(u, v+w) = (u, v) + (u, w)##. Perhaps I could use that ...
 
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1. What is an inner product in linear algebra?

An inner product is a mathematical operation that takes two vectors and produces a scalar value. It is often used in linear algebra to measure the angle between two vectors or to find the length of a vector.

2. How is an inner product calculated?

An inner product is calculated by taking the dot product of two vectors. This involves multiplying the corresponding components of each vector and then adding them together.

3. What is the significance of the inner product in linear algebra?

The inner product is important in linear algebra because it allows us to define important concepts such as orthogonality, projection, and distance. It also helps us solve systems of equations, find eigenvalues and eigenvectors, and perform other important operations.

4. Can an inner product be negative?

Yes, an inner product can be negative. This occurs when the angle between two vectors is obtuse, resulting in a negative value for the inner product. However, the inner product is typically defined to be positive when the angle between two vectors is acute.

5. How is the inner product related to the norm of a vector?

The inner product is related to the norm of a vector through the Pythagorean theorem. The norm of a vector is the square root of the inner product of the vector with itself. In other words, the norm of a vector is the length of the vector, and the inner product is used to calculate this length.

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