Zero Dimensional Null Space (What's the meaning of this?)

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Discussion Overview

The discussion revolves around understanding the concept of the null space and its dimension in the context of linear algebra, specifically regarding a given matrix A. Participants explore the implications of having zero free variables in the null space and the relationship between the null space and the zero vector.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant states that the dimension of Nul(A) is determined by the number of free variables, concluding that it is 0 due to the absence of free variables.
  • Another participant agrees that dim(Nul(A)) is 0, explaining that this means the null space consists solely of the zero vector, but notes that it could potentially include other vectors.
  • A participant questions whether a row of zeros can be discarded in the row-reduced form, leading to a discussion about the equivalence of matrices and the implications for dimensions.
  • There is a suggestion that if a free variable were present, it would indicate a line (dimension 1) or a plane (dimension 2) in the null space.
  • One participant expresses confusion about the relationship between free variables and the dimensionality of the null space, seeking a clearer understanding of the concept.
  • Another participant emphasizes that the null space is a vector space and provides an example to illustrate the properties of vector spaces.

Areas of Agreement / Disagreement

Participants generally agree that the dimension of Nul(A) is 0, indicating that the null space consists of the zero vector. However, there is some disagreement and confusion regarding the implications of free variables and the interpretation of the null space, suggesting that the discussion remains unresolved in certain aspects.

Contextual Notes

There are unresolved questions regarding the treatment of zero rows in row-reduced matrices and the implications for the dimensionality of the null space. Additionally, participants express varying levels of understanding about the concept of free variables and their relationship to the null space.

kosovo dave
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So a question on my linear algebra homework asks for the dimensions of Nul(A) and Col(A).
Let A =
\begin{pmatrix}
-4 & -3\\
-1 &4\\
-3& -7
\end{pmatrix}

I row reduced the above matrix to
\begin{pmatrix}
1 & 0\\
0 & 1\\
\end{pmatrix}

Now, the T.A. for my section told us that to find the dimension of Nul(A) you look at the number of free variables in Nul(A). There are no free variables, so the dimension of Nul(A) is 0? What does this mean? I think I may be a little confused on what it means to find the dimension of a space. Why should the number of free variables in the null space tell you anything about the dimension of the null space?
 
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kosovo dave said:
So a question on my linear algebra homework asks for the dimensions of Nul(A) and Col(A).
Let A =
\begin{pmatrix}
-4 & -3\\
-1 &4\\
-3& -7
\end{pmatrix}

I row reduced the above matrix to
\begin{pmatrix}
1 & 0\\
0 & 1\\
\end{pmatrix}
You should have another zero row in your matrix.
kosovo dave said:
Now, the T.A. for my section told us that to find the dimension of Nul(A) you look at the number of free variables in Nul(A). There are no free variables, so the dimension of Nul(A) is 0? What does this mean?
Yes, dim(Nul(A)) is 0. It means that the nullspace is just the zero vector. The null space will always contain the zero vector, but could have other vectors as well.
kosovo dave said:
I think I may be a little confused on what it means to find the dimension of a space. Why should the number of free variables in the null space tell you anything about the dimension of the null space?

Your matrix represents a transformation from ##\mathbb{R}^2## to ##\mathbb{R}^3##. In finding the nullspace, the matrix you ended with says that x = 0 and y = 0. There are only two variables, so there are no free variables that can take on arbitrary values. If you had ended up with a free variable, it would mean that the nullspace is a line (dimension 1) through the origin. If you had ended up with two free variables, the nullspace would be a plane (dimension 2) through the origin, and so on.
 
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Since I only have 2 variables, can't I throw away that row of 0's in the RREF matrix?

Mark44 said:
You should have another zero row in your matrix.
Yes, dim(Nul(A)) is 0. It means that the nullspace is just the zero vector. The null space will always contain the zero vector, but could have other vectors as well.Your matrix represents a transformation from ##\mathbb{R}^2## to ##\mathbb{R}^3##. In finding the nullspace, the matrix you ended with says that x = 0 and y = 0. There are only two variables, so there are no free variables that can take on arbitrary values. If you had ended up with a free variable, it would mean that the nullspace is a line (dimension 1) through the origin. If you had ended up with two free variables, the nullspace would be a plane (dimension 2) through the origin, and so on.

I still think I'm missing something here. I don't quite see why a free variable makes the null space a line, plane, etc. Maybe I'm approaching what the null space is in the wrong way. I'm thinking about it as the set of vectors you can put into x so that Ax=0. Is there a better way to think about it?
 
kosovo dave said:
I'm thinking about it as the set of vectors you can put into x so that Ax=0. Is there a better way to think about it?

It's best to keep in mind that the set of such vectors is itself a vector space. For example Ax = 0 and Ay = 0 implies A(x + y)\ = \ Ax + Ay \ = \ 0 + 0 = 0 . If you are thinking of vectors as directed line segments originating at the origin of Euclidean 3-D space then the possible proper subspaces are lines, planes, and the zero dimensional space.
 
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kosovo dave said:
Since I only have 2 variables, can't I throw away that row of 0's in the RREF matrix?
When you row reduce a matrix, you get another matrix that is equivalent to the one you start with. A 3 x 2 matrix can't be equivalent to a 2 x 2 matrix.
 
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