The square root of a 2x2 matrix

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Discussion Overview

The discussion centers around the calculation of the square root of a symmetric 2x2 matrix, exploring methods for deriving the square root, particularly through diagonalization and the properties of eigenvalues. The scope includes theoretical approaches and mathematical reasoning.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant suggests that a symmetric 2x2 matrix can be diagonalized, leading to a potential solution for the square root in the form of a diagonal matrix derived from the eigenvalues.
  • Another participant outlines the process of diagonalizing the matrix, taking the square root of the diagonal entries, and then converting back to the original basis, emphasizing the requirement for the matrix to be positive semidefinite.
  • There is a query regarding a specific external source and whether it assumes the eigenvalues of the matrix are denoted as r_1 and r_2.
  • Further clarification is sought on whether the identity matrix is referenced in the context of the external source's steps.

Areas of Agreement / Disagreement

Participants generally agree on the diagonalization approach to finding the square root of a symmetric 2x2 matrix, but there are questions about specific assumptions made in external references, indicating some uncertainty and lack of consensus on those points.

Contextual Notes

The discussion does not resolve the assumptions regarding the identity matrix or the specific eigenvalues mentioned in the external source, leaving these points open for further clarification.

onako
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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 [tex]B = P^{-1}DP[/tex], 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 [tex]X^2 = B[/tex], so it's reasonable to expect that X is on the form [tex]P^{-1}D^{\prime}P[/tex] for some diagonal matrix [tex]D^{\prime}[/tex].

Now, we try to solve the equation:

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

Now you solve this easy equation for [tex]D^{\prime}[/tex], 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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