Vector Space, Two bases related by Unitary Matrix

In summary, the conversation discusses the relationship between two orthonormal bases in a vector space equipped with a hermitian scalar product. It is shown that the matrix U with entries Uij is unitary, and the conversation ends with a discussion on how to prove this fact.
  • #1
Zatman
86
0

Homework Statement


The vector space V is equipped with a hermitian scalar product and an orthonormal basis e1, ..., en. A second orthonormal basis, e1', ..., en' is related to the first one by

[itex]\mathbf{e}_j^{'}= \displaystyle\sum_{i=1}^n U_{ij}\mathbf{e}_i[/itex]

where Uij are complex numbers. Show that Uij = <ei, ej'>, and that the matrix U with entries Uij is unitary.

2. The attempt at a solution
I have done the first part, by simply taking the scalar product with some ek of each side of the defining equation for ej', and using the fact that each term is zero unless i=k, which leads to the first result.

I'm not quite sure how to go about the second part. I've attempted to show that UU* = I; but all I can see is that the diagonal elements of UU* are:

[itex]\displaystyle\sum_{j=1}^n \left\langle \mathbf{e}_i, \mathbf{e}_j^{'} \right\rangle ^2[/itex]

for i = 1:n. I think that this probably could be shown to be 1 for all i using the definition given.

I'm not sure how the remaining elements are going to be zero though, which is necessary for UU* = I.

Any nudges in the right direction would be greatly appreciated!
 
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  • #2
I haven't worked this out, so this is just a suggestion. It seems you haven't used the fact that the second basis is orthonormal yet. What do you get if you use the fact that ##\langle \mathbf{e}'_i, \mathbf{e}'_j \rangle = \delta_{ij}##?
 
  • #3
Sorry, but I'm not sure how that can be used. The elements of U are scalar products of the ith basis vector and the jth primed basis vector, and the only relationship between the two bases is U itself.

What you wrote was used in a previous part of the question which I didn't bother typing out (it's not relevant to this part). Thank you, anyway.
 
  • #4
U looks like a linear transformation that maps

[itex]e_i \ to \ e'_i[/itex], so it preserves scalar products.

Then show that <U*Ux.y> = <Ux.Uy> = <x.y>
 
  • #5
Zatman said:
Sorry, but I'm not sure how that can be used. The elements of U are scalar products of the ith basis vector and the jth primed basis vector, and the only relationship between the two bases is U itself.

What you wrote was used in a previous part of the question which I didn't bother typing out (it's not relevant to this part). Thank you, anyway.
No, it wasn't. I wrote the inner product of ##e'_i## and ##e'_j##, both primed, not one primed and one unprimed.
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and a set of operations that can be performed on those vectors. These operations include addition and scalar multiplication, which allow for the combination and manipulation of vectors.

2. What is a basis in a vector space?

A basis is a set of linearly independent vectors that can be used to represent any vector in a vector space. This means that any vector in the space can be written as a unique linear combination of the basis vectors.

3. How are two bases related by a unitary matrix?

A unitary matrix is a special type of matrix that preserves the length and angles of vectors when multiplied by them. When a unitary matrix is used to transform one basis to another, the resulting basis will also be a valid basis for the vector space.

4. What is the importance of unitary matrices in vector spaces?

Unitary matrices are important in vector spaces because they allow for the transformation between different bases. This is useful for solving problems and making calculations in different bases, as well as for understanding the relationship between different bases in a vector space.

5. How can the relationship between two bases be represented using a unitary matrix?

The relationship between two bases can be represented using a unitary matrix by multiplying the original basis vectors by the unitary matrix. This will result in the new basis vectors, which will be related to the original basis vectors by the unitary matrix.

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