Question about Hermitian matrices

In summary, the conversation discusses the attempt to prove that the product of two vectors in a complex space, defined as <x,y> = xAy^*, is an inner product when A is a Hermitian matrix. However, it is revealed that this is not true in general and A must be positive definite for it to be an inner product. The concept of positive definite matrices is briefly explained and their role in defining inner products is mentioned.
  • #1
Bipolarity
776
2
I am trying to prove that for any two vectors x,y in ##ℂ^{n}## the product ## \langle x,y \rangle = xAy^{*} ## is an inner product where ##A## is an ##n \times n## Hermitian matrix.

This is actually a generalized problem I created out of a simpler textbook problem so I am not even sure if this is true although I believe it is true.

I proved most of the axioms for an inner product space, except the axiom that ## \langle x,x \rangle > 0 ## if ## x ≠ 0 ##. This is giving me trouble, since I first had to prove that ##<x,x>## is real (which I have done) but am still having trouble to actually prove that it is positive.

Any tips? Thanks!

BiP
 
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  • #3
micromass said:
What you're trying to prove is false. It won't be an inner product in general. You need ##A## to be positive-definite.

http://en.wikipedia.org/wiki/Positive-definite_matrix

Ah I see micro! I hadn't gotten there yet in my coursework. Could you give me a counterexample please? thanks.

BiP
 
  • #4
Bipolarity said:
Ah I see micro! I hadn't gotten there yet in my coursework. Could you give me a counterexample please? thanks.

BiP

It should be very easy for you to find a counterexample. You can even look for a ##1\times 1##-matrix as a counterexample.
 
  • #5
Positive definite matrices are defined to give the property you are seeking.

By their definition, xAx' > 0 for all vectors x if A is positive definite and these matrices act like norms and metrics in the way they transform vectors (and are used in situations that have this property like inner products in geometry and variance/co-variance in probability/statistics).
 

1. What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. This means that the matrix is symmetric about its main diagonal and the complex conjugate of each element is equal to the corresponding element in the transpose of the matrix. In other words, the matrix is equal to its own reflection across its main diagonal.

2. What are the properties of a Hermitian matrix?

Some key properties of a Hermitian matrix include: it is always square, it has real-valued entries along the main diagonal, and the complex conjugate of each element is equal to the corresponding element in the transpose of the matrix.

3. How is a Hermitian matrix related to a symmetric matrix?

A Hermitian matrix is essentially the complex version of a symmetric matrix. Both types of matrices have the property of being equal to their own transpose, but a Hermitian matrix includes complex numbers while a symmetric matrix only includes real numbers.

4. What is the significance of Hermitian matrices in quantum mechanics?

In quantum mechanics, Hermitian matrices are used to represent observables, such as position, momentum, and energy. These matrices have the property that all of their eigenvalues are real, making them useful for predicting the outcomes of quantum measurements.

5. How are Hermitian matrices used in data analysis?

Hermitian matrices are commonly used in data analysis to perform principal component analysis (PCA). PCA is a statistical technique used to reduce the dimensionality of a dataset by finding the most important features. Hermitian matrices are used to calculate the eigenvalues and eigenvectors of a dataset, which can then be used for dimensionality reduction and other data analysis tasks.

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