What exactly is a unique subspace?
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I am trying to solve a problem that is asking for what conditions that make $<A>$ unique. It does not explain what <> represents, but I think it means the column space. A is a matrix where its columns are a subset of the k largest eigenvectors of B.
I do not understand what it means for the column space to be unique. But if I had to guess, I would take it to mean that A is full rank, i.e., the subset of eigenvectors that form A are linearly independent.
Does anyone have a different interpretation?
Edit: Actually I just confirmed that $<A>$ is the column space of A.
matrices eigenvalues-eigenvectors independence matrix-rank
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I am trying to solve a problem that is asking for what conditions that make $<A>$ unique. It does not explain what <> represents, but I think it means the column space. A is a matrix where its columns are a subset of the k largest eigenvectors of B.
I do not understand what it means for the column space to be unique. But if I had to guess, I would take it to mean that A is full rank, i.e., the subset of eigenvectors that form A are linearly independent.
Does anyone have a different interpretation?
Edit: Actually I just confirmed that $<A>$ is the column space of A.
matrices eigenvalues-eigenvectors independence matrix-rank
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am trying to solve a problem that is asking for what conditions that make $<A>$ unique. It does not explain what <> represents, but I think it means the column space. A is a matrix where its columns are a subset of the k largest eigenvectors of B.
I do not understand what it means for the column space to be unique. But if I had to guess, I would take it to mean that A is full rank, i.e., the subset of eigenvectors that form A are linearly independent.
Does anyone have a different interpretation?
Edit: Actually I just confirmed that $<A>$ is the column space of A.
matrices eigenvalues-eigenvectors independence matrix-rank
I am trying to solve a problem that is asking for what conditions that make $<A>$ unique. It does not explain what <> represents, but I think it means the column space. A is a matrix where its columns are a subset of the k largest eigenvectors of B.
I do not understand what it means for the column space to be unique. But if I had to guess, I would take it to mean that A is full rank, i.e., the subset of eigenvectors that form A are linearly independent.
Does anyone have a different interpretation?
Edit: Actually I just confirmed that $<A>$ is the column space of A.
matrices eigenvalues-eigenvectors independence matrix-rank
matrices eigenvalues-eigenvectors independence matrix-rank
asked Nov 20 at 21:34
Iamanon
1157
1157
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1 Answer
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The column space is unique as it is defined as the smallest (wrt inclusion) subspace that contains all the columns of $A$ as elements. In this case it also is the set of all linear combinations of columns of $A$, which is minimal (as we can check it is a subspace and any subspace containing the columns must contain their linear combinations).
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
|
show 1 more comment
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
The column space is unique as it is defined as the smallest (wrt inclusion) subspace that contains all the columns of $A$ as elements. In this case it also is the set of all linear combinations of columns of $A$, which is minimal (as we can check it is a subspace and any subspace containing the columns must contain their linear combinations).
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
|
show 1 more comment
up vote
1
down vote
accepted
The column space is unique as it is defined as the smallest (wrt inclusion) subspace that contains all the columns of $A$ as elements. In this case it also is the set of all linear combinations of columns of $A$, which is minimal (as we can check it is a subspace and any subspace containing the columns must contain their linear combinations).
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
|
show 1 more comment
up vote
1
down vote
accepted
up vote
1
down vote
accepted
The column space is unique as it is defined as the smallest (wrt inclusion) subspace that contains all the columns of $A$ as elements. In this case it also is the set of all linear combinations of columns of $A$, which is minimal (as we can check it is a subspace and any subspace containing the columns must contain their linear combinations).
The column space is unique as it is defined as the smallest (wrt inclusion) subspace that contains all the columns of $A$ as elements. In this case it also is the set of all linear combinations of columns of $A$, which is minimal (as we can check it is a subspace and any subspace containing the columns must contain their linear combinations).
answered Nov 20 at 21:43
Henno Brandsma
103k345111
103k345111
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
|
show 1 more comment
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
Thanks. So if $A$ is full rank, then the column space of A is unique?
– Iamanon
Nov 20 at 21:44
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
@Iamanon It's always unique, but with full rank it is equal to the maximal subspace (the whole vector space the columns lie in), so $mathbb{R}^n$ in the case of a real-valued $n times m$ matrix.
– Henno Brandsma
Nov 20 at 21:47
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
Oh I see. Then it seems the question does not make sense. The question is asking for me to give a condition that makes the subspace <A> unique, where <A> is colspan(A). Since <A> is defined as colspan(A), then by definition <A> is unique?
– Iamanon
Nov 20 at 21:51
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
@Iamanon yes that's a nonsensical question. If we assume all the columns are linearly independent we at least can say that every element can be written in a unique way as a linear combination of columns. But that's not the same as unicity of the subspace,
– Henno Brandsma
Nov 20 at 21:53
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
Actually, $<A>$ is defined as the column SPAN (not space) of A. The column space is unique., but I think the column span need not be composed of the span of linearly independent columns. For example, if A is rank 3. Then we can still say that the column span is the span of the 3 columns of A. But this would not be the column space of A. The column space of A would be the span of the two linearly independent columns.
– Iamanon
Nov 20 at 21:55
|
show 1 more comment
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