Ryan's question at Yahoo Answers (Eigenvalues of A^*A)

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In summary, we proved that all eigenvalues of A*A are nonnegative and that A*A + I is invertible. This was done by showing that the eigenvalues satisfy a certain equation and that the determinant of A*A + I is non-zero. If you have any additional questions, feel free to post them in the appropriate section for further discussion.
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Fernando Revilla
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Here is the question:

Let A be a mxn matrix. Prove that all eigenvalues of A*A are nonnegative and prove that A*A + I is invertible.

THANK YOU!

Here is a link to the question:

Linear Algebra Proof Help Please? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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Hello Ryan,

Suppose $\lambda$ is an eigenvalue of $A^*A\in \mathbb{C}^{n\times n}$ then, there exists $x=(x_1,\ldots,x_n)^T\in\mathbb{C}^n$ such that $A^*Ax=\lambda x$. Multypling both sides by $x^*=(\overline{x_1},\ldots,\overline{x_n})$ we get $$x^*A^*Ax=(Ax)^*(Ax)=\lambda x^*x$$ But $y=Ax=(y_1,\ldots,y_m)^T$ and $x\neq 0$ (i.e. $||x||\neq0$), so $$\lambda=\frac{y^*y}{x^*x}=\frac{||y||^2}{||x||^2}\ge 0$$ On the other hand, $$\det (A^*A+I)=0\Leftrightarrow \det (A^*A-(-1)I)=0\Leftrightarrow -1\mbox{ is eigenvalue of }A^*A$$ This is a contradiction, so $\det(A^*A+I)\neq0$, as a consequence $A^*A+I$ is invertible.

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FAQ: Ryan's question at Yahoo Answers (Eigenvalues of A^*A)

What are eigenvalues and how are they related to matrices?

Eigenvalues are a mathematical concept used to describe the behavior of linear systems. In the context of matrices, eigenvalues are the values that satisfy the equation Ax = λx, where A is a square matrix, x is a vector, and λ is a scalar. In other words, they are the values that, when multiplied by a vector, result in a new vector that is parallel to the original vector. Eigenvalues are important because they help us understand the behavior and characteristics of a matrix.

What is the purpose of finding the eigenvalues of A^*A?

The eigenvalues of A^*A are important because they can tell us about the properties of the matrix A. For example, the eigenvalues of A^*A can give us information about the size, shape, and orientation of A. They can also help us solve systems of linear equations and understand the stability of a system.

How can I calculate the eigenvalues of A^*A?

The eigenvalues of A^*A can be calculated by first finding the eigenvalues of A, then taking the absolute value of each eigenvalue, and finally squaring each value. In other words, the eigenvalues of A^*A are the squared absolute values of the eigenvalues of A. This can be done using various mathematical methods, such as using determinants or eigenvector matrices.

Why is it important to consider the conjugate transpose (A^*) of A when finding eigenvalues?

The conjugate transpose (A^*) is important because it allows us to work with complex numbers when finding eigenvalues. In many scientific and engineering applications, matrices and vectors often contain complex numbers. By considering the conjugate transpose, we can accurately find the eigenvalues of a complex matrix A^*A, which can provide valuable insights and solutions to problems.

Can the eigenvalues of A^*A be negative?

Yes, the eigenvalues of A^*A can be negative. In fact, the eigenvalues of A^*A can be any real number, positive or negative. The sign of the eigenvalues can provide information about the properties of the matrix A, such as its invertibility and its stability. However, in some cases, the eigenvalues of A^*A may be complex numbers, which is why it is important to consider the conjugate transpose when finding eigenvalues.

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