How to Find Orthonormal Bases of Kernel and Row Space of Matrix A"

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In summary, to find the orthonormal bases of the kernel and row space, the array is reduced to its RREF and then divided by its length to get the orthonormal bases. However, it is important to check that the basis is actually orthogonal. Alternatively, the Gram-Schmidt process can be used to construct an orthogonal basis for the vector space, from which the orthonormal bases can be obtained. The basis for the row space is not equal to the original array, but is a set of vectors that span the same space.
  • #1
UrbanXrisis
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[tex]A = \left(\begin{array}{cccc}-1 &6&5&9 \\ -1&0&1&3 \end{array}\right)[/tex]

Find orthonormal bases of the kernel, row space.

To find the bases, I did reduced the array to its RREF.

[tex]A = \left(\begin{array}{cccc}1 & 0&-1&-3\\ 0&1&2/3&1 \end{array}\right)[/tex]

Then the orthonormal bases would just be that divided by the length.

[tex]||v_1||=\sqrt{1+1+3^2}=\sqrt{11}[/tex]

[tex]||v_2||=\sqrt{1+(2/3)^2+1}=\sqrt{2.44444}[/tex]

so that means, the orthonormal bases would be:

[tex]A = \left(\begin{array}{cccc} \frac{1}{ \sqrt{11}} & 0&\frac{-1}{ \sqrt{11}}&\frac{-3}{ \sqrt{11}} \\0 & \frac{1}{ \sqrt{2.44444}} & \frac{.66666}{ \sqrt{2.44444}} &\frac{1}{ \sqrt{2.44444}}\end{array}\right)[/tex]

what exactly is the orthonormal bases of the kernel?
Also, isn't the row space the same as the vectors of the bases?
I think I also did something wrong in my calculations
 
Last edited:
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  • #2
If you are looking for an orthonormal basis, one thing to check is that your basis actually is orthogonal. The basis you have found for the row space is not orthogonal.

Given an arbitrary basis for a vector space, do you know how to construct an orthogonal basis for it via the Gram-Schmidt process?
 
  • #3
is what i did above the orthonormal row space? that is wrong as well, i don't know why... however:

using the Gram-Schmidt process, i still get an error:

[tex]A = \left(\begin{array}{cccc}-1 &6&5&9 \\ -1&0&1&3 \end{array}\right)=\left(\begin{array}{cc}W_1 &W_2 \end{array}\right)[/tex]

want to find an orthonormal basis [tex]R={U_1 ,U_2}[/tex]

[tex]U_1=\frac{W}{||W_1||}[/tex]
[tex]||W_1||=\sqrt{11}[/tex]
[tex]U_1=\frac {\left(\begin{array}{cccc}-1 &6&5&9 \end{array}\right)}{\sqrt{11}}[/tex]

[tex]U_2=\frac{W_2-<W_2 , U_1 > U_1}{||W_2-<W_2 , U_1 > U_1||}[/tex]

where [tex]W_2=\left(\begin{array}{cccc} -1&0&1&3 \end{array}\right)[/tex]

[tex]U_1=\frac {\left(\begin{array}{cccc}-1 &6&5&9 \end{array}\right)}{\sqrt{11}}[/tex]
[tex]U_1=\left(\begin{array}{cccc}-1/\sqrt{11} &6\sqrt{11}&5\sqrt{11}&9\sqrt{11} \end{array}\right)[/tex]

is the the correct set up to get an orthonormal basis?

Also, to get an orthonormal row space, what would I have to do?
 
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  • #4
A is not equal to (W1 W2). W1 and W2 are a basis for the row space of A, they are not equal to A when written like that because they are horizontal.

It looks like the method you have set up to find U1 and U2 is correct. What do you mean an "orthonormal row space"? You can find an orthonormal _basis_ U1, U2 for the row space just by finding the vector U2 according to the formula you've set up.
 
  • #5
I typed my W1 and W2 vectors wrong. W1 is supposed to represent the vector (-1 6 5 9) and W2 = (-1 0 1 3)

What do I get when I find U1 and U2? Is U1 and U2 the row space? or is U1 an U2 the orthonormal basis?
 

Related to How to Find Orthonormal Bases of Kernel and Row Space of Matrix A"

1. How do I find the orthonormal basis of the kernel of matrix A?

To find the orthonormal basis of the kernel of matrix A, you can use the Gram-Schmidt process. This involves finding a set of linearly independent vectors in the kernel of A and then using the Gram-Schmidt process to orthogonalize and normalize them.

2. What is the difference between the kernel and row space of a matrix?

The kernel of a matrix A is the set of all vectors that, when multiplied by A, result in the zero vector. The row space of A is the set of all linear combinations of the rows of A. In other words, the kernel contains all vectors that are mapped to zero by A, while the row space contains all possible outputs of A.

3. How can I determine the dimension of the row space of a matrix?

The dimension of the row space of a matrix A is equal to the number of linearly independent rows in A. You can determine this by performing row reduction on A and counting the number of non-zero rows in the reduced matrix.

4. Can I find the orthonormal basis of the row space of a matrix using the Gram-Schmidt process?

No, the Gram-Schmidt process can only be used to find the orthonormal basis of the kernel of a matrix. To find the orthonormal basis of the row space, you can use the transpose of A and follow the same steps as you would for the kernel.

5. Why is it important to find the orthonormal basis of the kernel and row space of a matrix?

Finding the orthonormal basis of the kernel and row space of a matrix can help us better understand the properties and behavior of the matrix. It can also be useful in solving systems of linear equations and in applications such as signal processing and data compression.

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