Positive Operator: Why Is \sqrt{A^*A} Positive?

In summary, the question is why is \sqrt{A^*A} positive, where A* is the adjoint of a linear operator A. The suggested approach is to first determine the rank of a column vector a and a row vector b, and then carry out singular value decomposition or Cholesky to obtain a positive semi-definite product. Alternatively, one can use the spectral decomposition of A*A, which is self-adjoint, to show that the square root of A*A is positive. This is based on the fact that every positive operator has a unique positive square root and A*A is a positive operator.
  • #1
Dragonfall
1,030
4
Given a linear operator A, why is [tex]\sqrt{A^*A}[/tex] positive? Where A* is the adjoint.
 
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  • #2
I'll give a matrix argument. First a simple question, take a column vector a, what is the rank ? What is the rank of a*a? Same question for a row vector b?

I would suggest writing the dimensions and checking when the condition is true then carry on with singular value decomposition or Cholesky if you like. you might end up with positive SEMI-definite product! but if you modify the definitions of course, you can recover the same result with some caution.
 
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  • #3
The way I'd approach it is that A*A is self-adjoint, and hence has a spectral decomposition, and hence the sqrt of A*A is [tex]\sum_a\sqrt{a}\left| a\right>\left< a\right|[/tex]. Somehow, this is positive.
 
  • #4
This follows from the two following facts/theorems:
1) Every positive operator (on a complex Hilbert space) has a unique positive square root, and
2) A*A is a positive operator.
 

1. What is a positive operator?

A positive operator is a mathematical term used in linear algebra and functional analysis. It refers to an operator (a function that maps one mathematical object to another) that produces only positive values when applied to any input. In other words, a positive operator is one that preserves positivity, meaning it never outputs negative or zero values.

2. Why is the square root of A*A used in defining a positive operator?

The square root of A*A is used in defining a positive operator because it is a way to ensure that the operator is positive. The square root of A*A (where A* is the adjoint or conjugate transpose of A) is always a positive operator, regardless of the original operator A. It is a common method to define positive operators in functional analysis.

3. How is the positivity of an operator related to its eigenvalues?

The positivity of an operator is closely related to its eigenvalues. In fact, a positive operator is one that has only positive eigenvalues. This means that when the operator is applied to any vector, the resulting vector will have a positive dot product with the original vector. This is a key property of positive operators and is used in many applications in mathematics and physics.

4. Can a positive operator have negative eigenvalues?

No, a positive operator cannot have negative eigenvalues. As mentioned earlier, positive operators only have positive eigenvalues. This is because the eigenvalues of an operator are closely related to its positivity, and a positive operator is one that preserves positivity in its operations.

5. What are some real-world applications of positive operators?

Positive operators have various applications in mathematics, physics, and engineering. They are used in quantum mechanics to describe physical systems and measure their properties. In image processing, positive operators are used to enhance images and remove noise. They are also used in optimization problems to find the most efficient solution. Additionally, positive operators are used in economics and finance to model and predict market behavior.

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