Rank, Dimension, Subsapce, Column Space

In summary: If U and W are subspaces of a vector space V, then the set of vectors that belong to either U or W is a subspace of V.
  • #1
kingwinner
1,270
0
1) True or False? If true, prove it. If false, prove that it is false or give a counterexample.
1a) If A is m x n, then A and (A^T)(A) have the same rank.
1b) Let A be m x n and X E R^n. If X E null [(A^T)(A)], then AX is in both col(A) and null(A^T).

[I believe it's true that AX is in null(A^T), but I am totally unsure whether AX is in col(A) or not!?]
1c) If U and W are subspaces of a vector space V, then the set of vectors that belong to either U or W is a subspace of V.



2) Prove that if A is an m x n matrix, then null(A)=[col(A^T)]^|
[Using dimension theorem, I proved that their dimensions are equal...but I have no idea how to prove that they ARE equal...]



These are also the past exams questions that I am having terrible trouble with. Can someone give me some advice/hints? For 1b) and 2), I am partially done, but how 1a)c) I have no clue...

Any help/hints is greatly appreciated!
 
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  • #2
1a) Have you proved that the column rank equals the row rank? Then the proof of of 1a is trivial.
1c) First of all, if you have two sets, what set operation fits into what you need, i.e. "the set of vectors that belong either to U or to W"?
 
  • #3
kingwinner said:

1c) If U and W are subspaces of a vector space V, then the set of vectors that belong to either U or W is a subspace of V.


Are you sure that's the entire question? If [tex]U[/tex] and [tex]W[/tex] are subspaces of a vector space [tex]V[/tex], then [tex]U \cup W[/tex] is a subspace if and only if [tex] U \subseteq W[/tex] or [tex]W \subseteq U[/tex]

2) Prove that if A is an m x n matrix, then null(A)=[col(A^T)]^|
[Using dimension theorem, I proved that their dimensions are equal...but I have no idea how to prove that they ARE equal...]

Now, the column space of A^T is the row space of A. So, you need to show that
(i) the nullspace of A is a subset of [row(A)]^| . ie, show that every element which belongs to the null space also belongs to [row(A)]^|.
(ii) [row(A)]^| is a subset of the nullspace of A. ie, every element u in [row(A)]^| is also in the null space.
 
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  • #4
radou said:
1a) Have you proved that the column rank equals the row rank? Then the proof of of 1a is trivial.
1c) First of all, if you have two sets, what set operation fits into what you need, i.e. "the set of vectors that belong either to U or to W"?

1a) I have learned that rank A=dim(colA)=dim(rowA), but how does that help?

1c) The union space is larger, so my guess is that it would still be a subspace, right?
 
  • #5
siddharth said:
Are you sure that's the entire question? If [tex]U[/tex] and [tex]W[/tex] are subspaces of a vector space [tex]V[/tex], then [tex]U \cup W[/tex] is a subspace if and only if [tex] U \subseteq W[/tex] or [tex]W \subseteq U[/tex]



Now, the column space of A^T is the row space of A. So, you need to show that
(i) the nullspace of A is a subset of [row(A)]^| . ie, show that every element which belongs to the null space also belongs to [row(A)]^|.
(ii) [row(A)]^| is a subset of the nullspace of A. ie, every element u in [row(A)]^| is also in the null space.
1c) Yes, so I guess the answer is "false". But why? I don't understand...


2) But how can I relate null A to (rowA)^|? I can think of no way of showing them to be equal...

Thanks!
 
  • #6
1b) How can I know whether AX is in col(A) or not?
 

Related to Rank, Dimension, Subsapce, Column Space

1. What is rank in linear algebra?

Rank in linear algebra is the number of linearly independent rows or columns in a matrix. It represents the maximum number of linearly independent vectors in a vector space or the number of pivot columns in a matrix. The rank of a matrix can be determined by performing row reduction operations to obtain its reduced row echelon form.

2. How is dimension related to rank?

Dimension is closely related to rank in linear algebra. In a vector space, the dimension is the number of basis vectors required to span the entire space. The rank of a matrix is equal to the number of pivot columns, which is also the number of linearly independent columns. Therefore, the rank of a matrix is equal to the dimension of its column space.

3. What is a subspace in linear algebra?

A subspace in linear algebra is a subset of a vector space that is closed under vector addition and scalar multiplication. This means that any linear combination of vectors in the subspace will also be in the subspace. A subspace must also contain the zero vector and be non-empty. The dimension of a subspace is defined as the number of vectors in a basis for the subspace.

4. What are the properties of a column space?

The column space of a matrix is the span of its column vectors. It is a subspace of the vector space in which the matrix operates. The column space is also known as the range of the matrix and contains all possible linear combinations of the column vectors. The dimension of the column space is equal to the rank of the matrix.

5. How is the column space related to the null space?

The column space and null space are complementary subspaces in linear algebra. The column space is the set of all vectors that can be obtained by multiplying the matrix by a vector, while the null space is the set of all vectors that are mapped to the zero vector when multiplied by the matrix. The dimension of the column space and null space together is equal to the number of columns in the matrix.

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