The Dependence of Norm on Basis in Vector Spaces

In summary: Orthonormal bases are bases that are orthogonal to each other. So, by considering the basis vectors as coordinate vectors with respect to themselves, you're recovering the standard basis.
  • #1
union68
140
0
Hello. My question is: does the norm on a space depend on the choice of basis for that space?

Here's my line of reasoning:

If the set of vectors [tex]V = \left\{ v_1,v_2\right\}[/tex] is a basis for the 2-dimensional vector space [tex]X[/tex] and [tex]x \in X[/tex], then let

[tex] \left(x\right)_V = \left( c_1,c_2\right)[/tex]

denote the component vector of [tex]x[/tex] with respect to the basis [tex]V[/tex]. Now, let [tex]E[/tex] be the standard basis for [tex]X[/tex]; i.e.,

[tex]E = \left\{ \left(1,0\right),\left(0,1\right)\right\}[/tex]. Suppose

[tex]\left(v_1\right)_E = \left(2,1\right),[/tex]

and

[tex] \left(v_2\right)_E = \left(0,1\right)[/tex].

If [tex]\left(x\right)_E = \left(2,3\right)[/tex], then

[tex] \left(x\right)_V = \left(1,2\right)[/tex].

However, if we use the standard euclidean norm, the norm of vector [tex]\left(x\right)_V[/tex] is [tex]\sqrt{5}[/tex], whereas the norm of [tex]\left(x\right)_E[/tex] is [tex]\sqrt{13}[/tex].

Is this a correct analysis? It seems correct, since the euclidean norm depends on the components of the vector, and the components depend on the choice of basis...but something seems fishy.

Thanks!
 
Physics news on Phys.org
  • #2
You are correct--the value of the norm of a vector is basis-dependent. You can see it easily by noticing that [tex]\{(2,0),(0,2)\}[/tex] is also a basis for [tex]\mathbb{R}^2[/tex]. But no matter which basis you choose, the norm will still be a norm (satisfy all the conditions), so it doesn't matter.
 
  • #3
Fantastic. Thanks for the quick response. However, this leads me to more questions and some trouble:

With the above set [tex]V[/tex], we would have

[tex]\left(v_1\right)_V = \left(1,0\right)[/tex]

and

[tex]\left(v_2\right)_V = \left(0,1\right)[/tex],

correct? But, [tex]\left|\left(v_1\right)_V\right| = \left|\left(v_2\right)_V\right| = 1[/tex]. Furthermore, if we were to take the dot product as our inner product, and if it were dependent on the choice of basis also, then

[tex] \left< \left(v_1\right)_V,\left(v_2\right)_V\right> = 0[/tex].

This would mean that [tex]V[/tex] were an orthonormal basis with respect to (no suprise) itself. Ha. But, clearly, with reference to the standard basis, it isn't.

This makes me wonder, there has to be a common standard by which we judge all basis sets, otherwise we run into the problem above: that every basis can be turned into an orthonormal basis by considering the basis vectors as coordinate vectors with respect to themselves.

I have never seen this addressed before in any text, which is why I think I'm making a terrible mistake.
 
  • #4
union68 said:
every basis can be turned into an orthonormal basis by considering the basis vectors as coordinate vectors with respect to themselves.

If you do this, you'll just recover the standard basis every time, so it shouldn't be surprising. If I understand you correctly.
 
  • #5
Tinyboss said:
If you do this, you'll just recover the standard basis every time, so it shouldn't be surprising. If I understand you correctly.

Yup, you understand me. This isn't a problem, though?

When we say that this or that that is/isn't an orthonormal basis, did we make that determination with respect to another basis? Because, as in my second post, every basis is an orthonormal basis with respect to itself.

For instance, it doesn't make any sense to me to say that

[tex]\left(x\right)_V = \left(1,2\right)[/tex],

but then to express the basis vectors as [tex]v_1 = \left(2,1\right)[/tex] and [tex]v_2 = \left(0,1\right)[/tex], where clearly those are component vectors with respect to the standard basis. Why would we not express [tex]v_1[/tex] and [tex]v_2[/tex] as component vectors with respect to themsevles, since that's the basis we're working in?
 

FAQ: The Dependence of Norm on Basis in Vector Spaces

What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, which can be added together and multiplied by numbers to create new vectors. These operations must follow certain rules, such as closure under addition and scalar multiplication, in order for the set to be considered a vector space.

What is a norm in a vector space?

A norm is a mathematical function that assigns a non-negative value to a vector, representing its magnitude or length. It can be thought of as a generalization of the absolute value function in one dimension. Norms are useful in vector spaces because they allow us to measure the distance between two vectors, as well as define the concept of orthogonality.

How is a basis defined in a vector space?

A basis is a set of vectors that span the entire vector space, meaning that any vector in the space can be written as a linear combination of the basis vectors. This allows us to represent any vector in the space using a finite number of basis vectors, making it much easier to work with. The number of basis vectors is known as the dimension of the vector space.

How do I find the basis for a given vector space?

To find the basis for a vector space, we need to identify a set of linearly independent vectors that span the space. This can be done through various methods, such as Gaussian elimination or the Gram-Schmidt process. It is important to note that there can be multiple valid choices for a basis in a given vector space.

Can any set of vectors be a basis for a vector space?

No, not every set of vectors can be a basis for a vector space. In order to be a valid basis, the vectors must be linearly independent and span the entire space. This means that no vector in the set can be written as a linear combination of the other vectors, and every vector in the space can be written as a linear combination of the basis vectors.

Similar threads

Replies
16
Views
2K
Replies
2
Views
863
Replies
4
Views
2K
Replies
21
Views
1K
Replies
4
Views
2K
Replies
7
Views
2K
Back
Top