Square Root of a 2x2 Matrix (by diagonalization)

In summary, to show that a 2x2 matrix has one square root, we can use diagonalization to find the square root. This involves finding the eigenvalues and eigenvectors of the matrix, forming the matrix V with the eigenvectors as its columns, finding the diagonalized matrix D, and then using the formula A1/2 = V D1/2 V-1 to find the square root of A.
  • #1
krozer
13
0

Homework Statement


Show that the -1 -2
4 -1


2x2 matrix has one square root.

Homework Equations



det(A-λI) to find Eigenvalues
(A-λI)v=0 to find Eigenvectors
A1/2 = V D1/2 V-1 to find the square root of A where V is the created matrix with the eigenvectors of A, and D is the diagonalized matrix A

The Attempt at a Solution



I know I can use diagonalization to find the square root:
Using the formula its eigenvalues are -1+(-32)1/2 and -1-(-32)1/2
But I don't know how to find the eigenvectors given that (-32)1/2 it's a complex number.
 
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  • #2


To find the eigenvectors, we can use the fact that for a 2x2 matrix, the eigenvectors are given by the columns of the matrix V in the formula A1/2 = V D1/2 V-1.

First, let's find the eigenvalues of the matrix A. We can use the characteristic polynomial det(A-λI) = 0 to find the eigenvalues. In this case, the characteristic polynomial is λ^2 + 2λ + 3 = 0, which has the solutions λ = -1+(-32)1/2 and λ = -1-(-32)1/2.

Next, we can find the eigenvectors for each eigenvalue. For the eigenvalue λ = -1+(-32)1/2, we can substitute this into the equation (A-λI)v=0 to get the following system of equations:

3x + 2y = 0
2x + 3y = 0

Solving this system, we get the eigenvector v1 = [2, -3].

Similarly, for the eigenvalue λ = -1-(-32)1/2, we get the eigenvector v2 = [2, 3].

Now, we can form the matrix V with these eigenvectors as its columns:
V = [2, 2; -3, 3].

Next, we can find the diagonalized matrix D by substituting the eigenvalues into the diagonal matrix D = diag(λ1, λ2):
D = diag(-1+(-32)1/2, -1-(-32)1/2) = [-1+(-32)1/2, 0; 0, -1-(-32)1/2].

Finally, we can use the formula A1/2 = V D1/2 V-1 to find the square root of A. Substituting the matrices we found, we get:
A1/2 = V D1/2 V-1 = [2, 2; -3, 3] [-1+(-32)1/2, 0; 0, -1-(-32)1/2]1/2 [2, -3; 2, 3] = [1, 0; 0,
 

FAQ: Square Root of a 2x2 Matrix (by diagonalization)

1. What is diagonalization of a matrix?

Diagonalization of a matrix is the process of finding a diagonal matrix that is similar to the original matrix. This means that the entries on the main diagonal of the diagonal matrix are the eigenvalues of the original matrix, and the corresponding columns of the diagonal matrix are the eigenvectors of the original matrix.

2. Why is diagonalization important?

Diagonalization is important because it simplifies the calculation of matrix powers and determinants, and allows us to solve systems of linear differential equations. It also helps us understand the behavior of a matrix and its eigenvalues and eigenvectors.

3. How do you find the square root of a 2x2 matrix by diagonalization?

To find the square root of a 2x2 matrix by diagonalization, we first diagonalize the matrix by finding its eigenvalues and eigenvectors. Then, we take the square root of each eigenvalue and use the corresponding eigenvectors to construct a new diagonal matrix. Finally, we transform the new diagonal matrix back to the original matrix to get the square root.

4. Can all 2x2 matrices be diagonalized?

Yes, all 2x2 matrices can be diagonalized as long as they have two distinct eigenvalues. This is because a 2x2 matrix has two eigenvectors, which can be used to construct a diagonal matrix.

5. What is the difference between the square root of a matrix and the diagonalization of a matrix?

The square root of a matrix is a specific matrix that, when multiplied by itself, gives the original matrix. On the other hand, diagonalization of a matrix is a process of finding a diagonal matrix that is similar to the original matrix. The diagonal matrix may or may not be the square root of the original matrix, depending on the eigenvalues and eigenvectors of the original matrix.

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