Understanding the Nullspace of a Matrix for Subspace U in R4

R1<->R20 3 -4 2 | 00 0 2 4 | 0RREF...0 3 0 -6| 00 0 2 4 | 0Leading variables: y, zNon-leading: x,u2z + 4u = 0z = -2u3y -6u = 0y = 2u(x,y,z,u) = (x,2u,-2u,u)
  • #1
negation
818
0

Homework Statement



Let U be the subspace of R4 given by:
U = the nullspace of the matrix

[0 0 2 4
0 3 -4 2]

The Attempt at a Solution



let v = (v1,...v4) and w = (w1...w4)
(0,0,2,4) = (λ1v1 + λ2v2 + λ3v3 + λ4v4)
(0,3,-4,2) = (λ1w1 + λ2w2 + λ3w3 + λ4w4)



I haven't come across such problem before nor were taught what a nullspace of a matrix is.
Could someone provide some explanation as to the question?
 
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  • #2
Surely, you could look up "nullspace" in the index of your textbook? The "nullspace" of a matrix is the set of all vectors that the matrix maps into the 0 vector (i.e. makes "null"). It is easy to show that the set of all such vectors is a subspace (if Au= 0 and Av= 0 then [itex]A(\alpha u+ \beta v)= \alpha Au+ \beta Av= 0[/itex]).

So here you are asked to find all [itex]\begin{bmatrix}x \\ y \\ z \\ u\end{bmatrix}[/itex] such that
[tex]\begin{bmatrix}0 & 0 & 2 & 4 \\ 0 & 3 & -4 & 2\end{bmatrix}\begin{bmatrix}x \\ y \\ z\\ u\end{bmatrix}= \begin{bmatrix}2z+ 4u \\ 3y- 4z+ 2u\end{bmatrix}= \begin{bmatrix}0 \\ 0 \end{bmatrix}[/tex]

Solve the equations 2z+ 4u= 0 and 3y- 4z+ 2u= 0. (There is not a unique answer.)
 
  • #3
HallsofIvy said:
Surely, you could look up "nullspace" in the index of your textbook? The "nullspace" of a matrix is the set of all vectors that the matrix maps into the 0 vector (i.e. makes "null"). It is easy to show that the set of all such vectors is a subspace (if Au= 0 and Av= 0 then [itex]A(\alpha u+ \beta v)= \alpha Au+ \beta Av= 0[/itex]).

So here you are asked to find all [itex]\begin{bmatrix}x \\ y \\ z \\ u\end{bmatrix}[/itex] such that
[tex]\begin{bmatrix}0 & 0 & 2 & 4 \\ 0 & 3 & -4 & 2\end{bmatrix}\begin{bmatrix}x \\ y \\ z\\ u\end{bmatrix}= \begin{bmatrix}2z+ 4u \\ 3y- 4z+ 2u\end{bmatrix}= \begin{bmatrix}0 \\ 0 \end{bmatrix}[/tex]

Solve the equations 2z+ 4u= 0 and 3y- 4z+ 2u= 0. (There is not a unique answer.)

It's not in the content of the book. Search engine showed rather dissappointing results too.
 
  • #4
HallsofIvy said:
Surely, you could look up "nullspace" in the index of your textbook? The "nullspace" of a matrix is the set of all vectors that the matrix maps into the 0 vector (i.e. makes "null"). It is easy to show that the set of all such vectors is a subspace (if Au= 0 and Av= 0 then [itex]A(\alpha u+ \beta v)= \alpha Au+ \beta Av= 0[/itex]).

So here you are asked to find all [itex]\begin{bmatrix}x \\ y \\ z \\ u\end{bmatrix}[/itex] such that
[tex]\begin{bmatrix}0 & 0 & 2 & 4 \\ 0 & 3 & -4 & 2\end{bmatrix}\begin{bmatrix}x \\ y \\ z\\ u\end{bmatrix}= \begin{bmatrix}2z+ 4u \\ 3y- 4z+ 2u\end{bmatrix}= \begin{bmatrix}0 \\ 0 \end{bmatrix}[/tex]

Solve the equations 2z+ 4u= 0 and 3y- 4z+ 2u= 0. (There is not a unique answer.)
0x + 0y +2z +4u = 0
0x + 3y -4z +2u = 0

0 0 2 4 | 0
0 3 -4 2 | 0

R1<->R2

0 3 -4 2 | 0
0 0 2 4 | 0

z and u are free variables. There exists infinite solutions to the SOLE.
(x,y,z,u) = (x,4/3z-2/3u,-2u,u)

The questions askes for a spanning set for U matrix. So if the null matrix (0,0,0,0) spans(x,y,z,u), then, (0,0,0,0) is a linear combination of (x,y,z,u).

Edit: λ1(x)+λ2(4/3z-2/3u) +λ3(-2u) + λ4(u) = (0,0,0,0)
 
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  • #5
negation said:
0x + 0y +2z +4u = 0
0x + 3y -4z +2u = 0

0 0 2 4 | 0
0 3 -4 2 | 0

R1<->R2

0 3 -4 2 | 0
0 0 2 4 | 0
The matrix above is not completely reduced. In fact, all you did was switch the two rows.
negation said:
z and u are free variables. There exists infinite solutions to the SOLE.
(x,y,z,u) = (x,4/3z-2/3u,-2u,u)

The questions askes for a spanning set for U matrix. So if the null matrix (0,0,0,0) spans(x,y,z,u), then, (0,0,0,0) is a linear combination of (x,y,z,u).
(0, 0, 0, 0) is the zero vector in R4. It does NOT span anything other than itself. In your sentence that starts "So if the null matrix ...", there is not one thing that is true.
negation said:
Edit: λ1(x)+λ2(4/3z-2/3u) +λ3(-2u) + λ4(u) = (0,0,0,0)

You need to pay more attention to definitions, especially those for "nullspace" and "span". Look in the index (at the back of the book) to see where these terms appear in the book. If a term is used on more than one page, it usually is defined on the first page of the list. The table of contents at the front of the book is probably less helpful, as it has a list of the chapters without much detail on what's in the chapter.
 
  • #6
Mark44 said:
The matrix above is not completely reduced. In fact, all you did was switch the two rows.
(0, 0, 0, 0) is the zero vector in R4. It does NOT span anything other than itself. In your sentence that starts "So if the null matrix ...", there is not one thing that is true. You need to pay more attention to definitions, especially those for "nullspace" and "span". Look in the index (at the back of the book) to see where these terms appear in the book. If a term is used on more than one page, it usually is defined on the first page of the list. The table of contents at the front of the book is probably less helpful, as it has a list of the chapters without much detail on what's in the chapter.

0 0 2 4 | 0
0 3 -4 2 | 0

R1<->R2

0 3 -4 2 | 0
0 0 2 4 | 0

RREF...

0 3 0 -6| 0
0 0 2 4 | 0

Leading variables: y, z
Non-leading: x,u

2z + 4u = 0
z = -2u
3y -6u = 0
y = 2u

(x,y,z,u) = (x,2u,-2u,u)
 
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  • #7
negation said:
0 0 2 4 | 0
0 3 -4 2 | 0

R1<->R2

0 3 -4 2 | 0
0 0 2 4 | 0

RREF...

0 3 0 -6| 0
0 0 2 4 | 0
The 4th number in the top row is wrong.
negation said:
Leading variables: y, z
Non-leading: x,u

2z + 4u = 0
z = -2u
3y -6u = 0
y = 2u

(x,y,z,u) = (x,2u,-2u,u)

You should check your work.
This matrix product ...
$$ \begin{bmatrix}0 & 0 & 2 & 4 \\ 0 & 3 & -4 & 2 \end{bmatrix} \begin{bmatrix}x \\ 2u \\ -2u \\ u \end{bmatrix}$$
should result in the column vector <0, 0>. Does it?
 
  • #8
Mark44 said:
The 4th number in the top row is wrong.


You should check your work.
This matrix product ...
$$ \begin{bmatrix}0 & 0 & 2 & 4 \\ 0 & 3 & -4 & 2 \end{bmatrix} \begin{bmatrix}x \\ 2u \\ -2u \\ u \end{bmatrix}$$
should result in the column vector <0, 0>. Does it?


0, 3, 0, 10 | 0
0, 0, 2, 4| 0

Leading variables: y, z
Non-leading variables: x, u

2z + 4u = 0
2z = -4u
z = -2u

3y + 10u = 0
y = -10u/3

(x,y,z,u) = (x,-10u/3,-2u,u)

[0, 0, 2, 4; 0, 0, 2, 4] [x; -10u/3; -2u;u] = [0;0]
 
  • #9
It's easy to make mistakes when you're reducing a matrix, so it's important to check your work (as I suggested in my previous post). Also, it's helpful to indicate in your work what you're doing. Off to the left of my matrices, if I'm adding -3 times row 2 to row 1 (for example), I put -3 to the left of row 2, and 1 to the left of row 1, with an arrow going from row 2 to row 1.

That way, if my answer doesn't check out, I can go back and look at my individual steps. Having an audit trail, so to speak, makes it easier to quickly check my row-reduction work.
 
  • #10
negation said:
0, 3, 0, 10 | 0
0, 0, 2, 4| 0

Leading variables: y, z
Non-leading variables: x, u

2z + 4u = 0
2z = -4u
z = -2u

3y + 10u = 0
y = -10u/3

(x,y,z,u) = (x,-10u/3,-2u,u)

[0, 0, 2, 4; 0, 0, 2, 4] [x; -10u/3; -2u;u] = [0;0]
That's more like it! Congrats!

Your solution could be written as <x, 0, 0, 0> + <0, -(10/3)u, -2u, u>, or better yet, as x<1, 0, 0, 0> + u<0, -10/3, -2, 1>, where x and u are arbitrary real numbers.

Since there are two vectors, the nullspace of your matrix is a two-dimensional subspace of R4. In other words, it is a plane in four dimensions. The vectors {<1, 0, 0, 0>, <0, -10/3, -2, 1>} span the nullspace, and since they are linearly independent (which I can tell by inspection), they form a basis for the nullspace.
 
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  • #11
Mark44 said:
It's easy to make mistakes when you're reducing a matrix, so it's important to check your work (as I suggested in my previous post). Also, it's helpful to indicate in your work what you're doing. Off to the left of my matrices, if I'm adding -3 times row 2 to row 1 (for example), I put -3 to the left of row 2, and 1 to the left of row 1, with an arrow going from row 2 to row 1.

That way, if my answer doesn't check out, I can go back and look at my individual steps. Having an audit trail, so to speak, makes it easier to quickly check my row-reduction work.

Just to clarify, and ensure I don't get lost in the intermediate step (I like looking at big picture).
Is the correct question I ought to be asking myself at the onset is:
Given [0, 0, 2, 4; 0, 0, 2, 4], what values must the vector[x;y;z;u] be such that
[0, 0, 2, 4; 0, 0, 2, 4][x;y;z;u] = [0;0] ?
The way I see it, [x;y;z;u] is really just the scalar in the linear combination.
 
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  • #12
Mark44 said:
That's more like it! Congrats!

Your solution could be written as <x, 0, 0, 0> + <0, -(10/3)u, -2u, u>, or better yet, as x<1, 0, 0, 0> + u<0, -10/3, -2, 1>, where x and u are arbitrary real numbers.

Since there are two vectors, the nullspace of your matrix is a two-dimensional subspace of R4. In other words, it is a plane in four dimensions. The vectors {<1, 0, 0, 0>, <0, -10/3, -2, 1>} span the nullspace, and since they are linearly independent (which I can tell by inspection), they form a basis for the nullspace.

Strange, the system rejected my answer.
It asked what the spanning set of U is.
(x,-10u/3,-2u,u) is apparently not the right one...
 
Last edited by a moderator:
  • #13
negation said:
Just to clarify, and ensure I don't get lost in the intermediate step (I like looking at big picture).
Is the correct question I ought to be asking myself at the onset is:
Given [0, 0, 2, 4; 0, 0, 2, 4], what values must the vector[x;y;z;u] be such that
[0, 0, 2, 4; 0, 0, 2, 4][x;y;z;u] = [0;0] ?
Yes.
negation said:
The way I see it, [x;y;z;u] is really just the scalar in the linear combination.
No, <x, y, z, u> is a vector in R4. Its coordinates (the x, y, z, and u values) are scalars.
 
  • #14
negation said:
Strange, the system rejected my answer.
It asked what the spanning set of U is.
(x,-10u/3,-2u,u) is apparently not the right one...
The spanning set would be a set of one or more vectors. What I wrote at the tail end of post #10 is probably what they're looking for.
 
  • #15
Mark44 said:
The spanning set would be a set of one or more vectors. What I wrote at the tail end of post #10 is probably what they're looking for.

They're looking for the vectors in format as such, e.g., (1,2,3,4), (5,6,7,8)
 
  • #16
negation said:
They're looking for the vectors in format as such, e.g., (1,2,3,4), (5,6,7,8)
See post 10. Note that I incorrectly copied one coordinate of one vector, but it's fixed now.
 

Related to Understanding the Nullspace of a Matrix for Subspace U in R4

1. What is the nullspace of a matrix?

The nullspace of a matrix is the set of all vectors that, when multiplied by the matrix, result in a zero vector. In other words, it is the set of all solutions to the equation Ax = 0, where A is the given matrix.

2. How is the nullspace of a matrix related to linear independence?

The nullspace of a matrix is closely related to linear independence. If a matrix has a nontrivial nullspace, it means that there are linearly dependent columns in the matrix. This is because the nullspace contains all vectors that can be written as a linear combination of the columns of the matrix.

3. Can the nullspace of a matrix have more than one dimension?

Yes, the nullspace of a matrix can have more than one dimension. In fact, the dimension of the nullspace is equal to the number of free variables in the system of equations Ax = 0. This is known as the nullity of the matrix.

4. How do you find the nullspace of a matrix?

To find the nullspace of a matrix, you can use row reduction techniques to put the matrix into reduced row echelon form. The variables corresponding to the pivot columns will be the basic variables, while the variables corresponding to the non-pivot columns will be the free variables. You can then express the basic variables in terms of the free variables to find the nullspace.

5. What is the importance of the nullspace in matrix operations?

The nullspace is important in matrix operations because it allows us to solve systems of linear equations, determine linear independence, and find the rank of a matrix. It also helps us understand the properties and behavior of matrices, such as invertibility and singularity.

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