How they found the left nullspace in each of these examples

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In summary, the left nullspace of a matrix can be found by performing row reduction on the transpose of the matrix. Its purpose is to find solutions to homogeneous systems of linear equations and provide information about the linear independence and rank of the matrix. It can be empty if the rank of the matrix is equal to the number of columns. Finding the left nullspace differs from finding the right nullspace in the method used and the type of equations it represents. The left nullspace can contain non-zero vectors, but these vectors will be orthogonal to all the rows of the matrix and will not contribute to the solutions of any system of equations involving the matrix.
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LongApple
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Homework Statement



Part b)

http://www.math.utah.edu/~zwick/Classes/Fall2012_2270/Lectures/Lecture19_with_Examples.pdf
upload_2015-1-22_11-56-16.png
For B
upload_2015-1-22_11-57-0.png


Left nullspace is solution to A ^ T times Y =0
So we have a free variable for the third row so don't we have infinitely many solutions as x3 could be anything?
upload_2015-1-22_11-51-3.png


In this problem's part b) I don't think that they took the transpose of the matrix A.

http://staff.imsa.edu/~fogel/LinAlg/PDF/29 Fundamental Subspaces.pdf
Go the bottom of page 1 under 4) and you'll see "
Perform Gaussian (or better, Gauss-Jordan) elimination on [A | I] to produce [U | B] (or
[R | C]). Claim: the last m – r rows of B (which equal those of C because once we get zeroes
there’s no more work to do in those rows) form ..."

So why isn't the last three rows of 0's in A just the left nullspace?

I am trying to figure out why

Homework Equations

The Attempt at a Solution

 

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  • #2
LongApple said:
Left nullspace is solution to A ^ T times Y =0
So we have a free variable for the third row so don't we have infinitely many solutions as x3 could be anything?
B is a 2x3 matrix, so it maps vectors from ##\mathbb{R}^3## to ##\mathbb{R}^2##. In which of these two vector spaces does the left nullspace reside?

In this problem's part b) I don't think that they took the transpose of the matrix A.

http://staff.imsa.edu/~fogel/LinAlg/PDF/29 Fundamental Subspaces.pdf
Go the bottom of page 1 under 4) and you'll see "
Perform Gaussian (or better, Gauss-Jordan) elimination on [A | I] to produce [U | B] (or
[R | C]). Claim: the last m – r rows of B (which equal those of C because once we get zeroes
there’s no more work to do in those rows) form ..."

So why isn't the last three rows of 0's in A just the left nullspace?
You start with the (untransposed) A and form the augmented matrix [A | I] and perform row operations to produce [U | B]. The basis vectors are the last m-r rows of B. So why are you thinking the rows of A have anything to do with the basis of the left nullspace? It should be clear the last three rows of A can't form basis because they're all 0s.
 
  • #3
vela said:
B is a 2x3 matrix, so it maps vectors from ##\mathbb{R}^3## to ##\mathbb{R}^2##. In which of these two vector spaces does the left nullspace reside?You start with the (untransposed) A and form the augmented matrix [A | I] and perform row operations to produce [U | B]. The basis vectors are the last m-r rows of B. So why are you thinking the rows of A have anything to do with the basis of the left nullspace? It should be clear the last three rows of A can't form basis because they're all 0s.

I don't think so.

But then what does this mean?
https://api.viglink.com/api/click?f...fogel/LinAlg/PDF/29 Fundamental Subspaces.pdf
Perform Gaussian (or better, Gauss-Jordan) elimination on [A | I] to produce [U | B] (or
[R | C]). Claim: the last m – r rows of B (which equal those of C because once we get zeroes
there’s no more work to do in those rows) form ..."
 
  • #4
Sorry for the late reply. I've been kinda busy this week.

I simply paraphrased the passage you quoted from the PDF, so I'm not sure what you're disagreeing with. As a result of the row operations, matrix A turns into U, and the identity matrix turns into B. The passage says the last rows of B then form a basis for the left nullspace.
 

FAQ: How they found the left nullspace in each of these examples

How do you find the left nullspace in these examples?

The left nullspace can be found by performing row reduction on the transpose of the matrix. The pivot columns of the transpose correspond to the linearly independent columns of the original matrix, and the remaining columns form the basis for the left nullspace.

What is the purpose of finding the left nullspace?

The left nullspace is useful for finding solutions to homogeneous systems of linear equations. It also provides information about the linear independence and rank of a matrix.

Can the left nullspace be empty?

Yes, the left nullspace can be empty if the rank of the matrix is equal to the number of columns, meaning there are no linearly independent columns. This indicates that the matrix is full rank and has a unique solution to every system of equations.

How does finding the left nullspace differ from finding the right nullspace?

The left nullspace is found by performing row reduction on the transpose of the matrix, while the right nullspace is found by performing column reduction on the original matrix. Additionally, the left nullspace represents the solutions to ATx = 0, while the right nullspace represents the solutions to Ax = 0.

Can the left nullspace contain non-zero vectors?

Yes, the left nullspace can contain non-zero vectors. However, these vectors will be orthogonal to all the rows of the matrix, meaning they will not contribute to the solutions of any system of equations involving the matrix.

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