Write down orthonormal bases for the four fundamental subspaces [ ]

In summary, to find orthonormal bases for the four fundamental subspaces of A = matrix([1,2],[3,6]]), you can use the Gram-Schmidt process to find the bases for the row and column spaces, and then use the equations Ax = 0 and xTA = 0 to find the bases for the nullspace and left nullspace.
  • #1
s3a
818
8
Write down orthonormal bases for the four fundamental subspaces [...]"

Homework Statement


Problem:
Write down orthonormal bases for the four fundamental subspaces of A = matrix([1,2],[3,6]]). (1 and 2 are on the first row whereas 3 and 6 are on the second row.)

Solution:
A = matrix([1,2],[3,6]]) is a 2 by 2 matrix of rank 1. Its row space has basis ##v_1##, its nullspace has basis ##v_2##, its column space has basis ##u_1##, its left null space has basis ##u_2##:

Row space: 1/sqrt(5) matrix([1],[2])
Nullspace: 1/sqrt(5) matrix([2],[-1])
Column space: 1/sqrt(10) matrix([1],[3])
Left nullspace: 1/sqrt(10) matrix([3],[-1])

Homework Equations


Gram-Schmidt process (I think)

The Attempt at a Solution


I watched videos on the Gram-Schmidt process but, they involve vectors whereas this involves a matrix, plus the concept with the vectors is still new to me so could someone help me with this super basic problem so that I can get started with the more complex ones please?

Any input would be greatly appreciated!
 
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  • #2
Actually, I figured out the row and column vector parts of this question but, I have no idea how to do the nullspace and left nullspace parts. It's probably a simple extension of the concept but, I don't know what to do. I have a feeling that this problem is really simple so, it should take minimal effort to help so, please do.
 
  • #3
s3a said:

Homework Statement


Problem:
Write down orthonormal bases for the four fundamental subspaces of A = matrix([1,2],[3,6]]). (1 and 2 are on the first row whereas 3 and 6 are on the second row.)

Solution:
A = matrix([1,2],[3,6]]) is a 2 by 2 matrix of rank 1. Its row space has basis ##v_1##, its nullspace has basis ##v_2##, its column space has basis ##u_1##, its left null space has basis ##u_2##:

Row space: 1/sqrt(5) matrix([1],[2])
Nullspace: 1/sqrt(5) matrix([2],[-1])
Column space: 1/sqrt(10) matrix([1],[3])
Left nullspace: 1/sqrt(10) matrix([3],[-1])

Homework Equations


Gram-Schmidt process (I think)

The Attempt at a Solution


I watched videos on the Gram-Schmidt process but, they involve vectors whereas this involves a matrix, plus the concept with the vectors is still new to me so could someone help me with this super basic problem so that I can get started with the more complex ones please?

Any input would be greatly appreciated!

s3a said:
Actually, I figured out the row and column vector parts of this question but, I have no idea how to do the nullspace and left nullspace parts. It's probably a simple extension of the concept but, I don't know what to do. I have a feeling that this problem is really simple so, it should take minimal effort to help so, please do.

For the nullspace, you're looking at the equation Ax = 0, and finding a basis for this set. For the left nullspace, you're looking at the equation xTA = 0, and finding a basis for this set. You show answers above. Are these from the back of the book, and you're uncertain how they were found?

BTW, LaTeX provides the nicest presentation of matrices, and it's not that hard. Here's your matrix:
$$ A = \begin{bmatrix} 1 & 2 \\ 3 & 6\end{bmatrix}$$
Right click the matrix above to see my LaTeX script.

For the left nullspace, this is the equation you need to work with:
$$ \begin{bmatrix}x_1 & x_2 \end{bmatrix} \begin{bmatrix}1 & 2 \\ 3 & 6\end{bmatrix} = 0$$
 

1. What are the four fundamental subspaces?

The four fundamental subspaces refer to the column space, null space, row space, and left null space of a matrix. These subspaces are important in linear algebra and have many applications in scientific fields, such as physics and engineering.

2. Why is it important to find orthonormal bases for these subspaces?

Orthonormal bases for the four fundamental subspaces provide a convenient and efficient way to represent and manipulate vectors within these subspaces. They also allow for easier computations and can help simplify complex problems in linear algebra.

3. How do you find orthonormal bases for the four fundamental subspaces?

The process of finding orthonormal bases for the four fundamental subspaces involves performing row operations on a matrix to reduce it to its reduced row echelon form. The columns of the resulting matrix form the basis for the column space, while the nonzero rows form the basis for the row space. The basis for the null space can be found by solving the system of equations represented by the reduced row echelon form, and the basis for the left null space can be found by taking the transpose of the reduced row echelon form and solving the resulting system of equations.

4. What is the significance of orthonormality in these bases?

Orthogonality and normality are important properties of orthonormal bases. Orthogonality means that the vectors in the basis are perpendicular to each other, while normality means that they have a length of 1. These properties make it easier to perform calculations and also allow for easier visualization of the vectors in the subspace.

5. Can orthonormal bases for the four fundamental subspaces be used in other applications besides linear algebra?

Yes, orthonormal bases for the four fundamental subspaces have many applications in various scientific fields. For example, they can be used in signal processing to represent and manipulate signals, in computer graphics to represent and transform objects, and in quantum mechanics to represent quantum states. They can also be used in machine learning and data analysis to extract important features from high-dimensional data.

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