The square root of a 2x2 matrix

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To calculate the square root of a symmetric 2x2 matrix, first diagonalize the matrix using its eigenvectors and eigenvalues. The matrix can be expressed as B = P^{-1}DP, where D contains the eigenvalues. The square root X of the matrix is then found in the form X = P^{-1}D'P, where D' is a diagonal matrix derived from the square roots of the eigenvalues in D. It is essential that the matrix is positive semidefinite, ensuring nonnegative eigenvalues for a valid square root. This method simplifies the calculation by leveraging the properties of diagonal matrices.
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Given a symmetric 2x2 matrix, what would be the way to calculate the square root of it?
Here, http://www.jstor.org/stable/2689616?seq=2 , it is announced that a relatively simple formula
could be applied for the resulting matrix entries, but I could not access it further.

Thanks
 
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A symmetric matrix B can be diagonalized.

Let B = P^{-1}DP, where P is the matrix with the eigenvector columns, and D is the matrix with the eigenvalues as diagonal entries.
You want a solution to X^2 = B, so it's reasonable to expect that X is on the form P^{-1}D^{\prime}P for some diagonal matrix D^{\prime}.

Now, we try to solve the equation:

X^2 = (P^{-1}D^{\prime}P)^2 = P^{-1}D^{\prime 2}P = B = P^{-1}DP \Rightarrow D^{\prime 2 } = D.

Now you solve this easy equation for D^{\prime}, hence determining X.
 
In simple words:
* diagonalize the matrix (i.e. move to a basis of eigenvectors)
* take the square root (which is now simple because the square root of a diagonal matrix is the diagonal matrix obtained from taking the square root of the diagonal entries),
* convert back to the original basis (if desired)

The matrix has to be positive semidefinite, so has to have nonnegative eigenvalues. But actually, all of this is probably in your definition of "square root of a matrix"?
 
onako said:
Is he assuming that r_1,r_2 are the eigenvalues of A?
Since he writes "Step 1. Find the roots r_1,r_2 ( eigenvalues)", the answer is yes.
In step 3, is I the identity matrix?
Yes.
 
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