Proving that the input space of a surjective linear transformation has a dimension at least as large as the...
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Say we have a linear transformation $T : V to W$. We know that $T$ is surjective or “onto $W$”.
I'm trying to prove that $n = dim(V) > m = dim(W)$. Intuitively, this makes sense. $V$ would have to be at least as “large” as $W$ in order for every vector in $W$ to be the image of at least one vector in $V$.
I'm having trouble sort of “wrapping up” this proof. Here's what I have.
Let $x in V, y in W$.
We know $x$ must be a linear combination of some basis for $V$. Say that basis is ${v_1, cdots, v_n}$. $x = c_1v_1 + cdots + c_nv_n$.
From the properties of a linear transformation we know that: $T(x) = c_1T(v_1) + cdots + c_nT(v_n) = y$.
This tells me that $y$ is a linear combination of the set ${T(v_1), cdots T(v_n)}$. This means that the some (non-strict) subset of ${T(v_1), cdots, T(v_n)}$ must form a basis for $W$. A basis for $W$ requires at least $m$ linearly independent vectors. Thus, $n geq m$.
My gut says this feels incomplete. I feel like I've made an assumption that the set ${T(v_1), cdots, T(v_n)}$ is linearly independent because we know ${v_1, cdots, v_n}$ is, but I haven't shown that. Any hints?
linear-algebra linear-transformations
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up vote
1
down vote
favorite
Say we have a linear transformation $T : V to W$. We know that $T$ is surjective or “onto $W$”.
I'm trying to prove that $n = dim(V) > m = dim(W)$. Intuitively, this makes sense. $V$ would have to be at least as “large” as $W$ in order for every vector in $W$ to be the image of at least one vector in $V$.
I'm having trouble sort of “wrapping up” this proof. Here's what I have.
Let $x in V, y in W$.
We know $x$ must be a linear combination of some basis for $V$. Say that basis is ${v_1, cdots, v_n}$. $x = c_1v_1 + cdots + c_nv_n$.
From the properties of a linear transformation we know that: $T(x) = c_1T(v_1) + cdots + c_nT(v_n) = y$.
This tells me that $y$ is a linear combination of the set ${T(v_1), cdots T(v_n)}$. This means that the some (non-strict) subset of ${T(v_1), cdots, T(v_n)}$ must form a basis for $W$. A basis for $W$ requires at least $m$ linearly independent vectors. Thus, $n geq m$.
My gut says this feels incomplete. I feel like I've made an assumption that the set ${T(v_1), cdots, T(v_n)}$ is linearly independent because we know ${v_1, cdots, v_n}$ is, but I haven't shown that. Any hints?
linear-algebra linear-transformations
1
Can you use the rank-nullity formula?
– Bernard
Nov 17 at 20:12
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
Say we have a linear transformation $T : V to W$. We know that $T$ is surjective or “onto $W$”.
I'm trying to prove that $n = dim(V) > m = dim(W)$. Intuitively, this makes sense. $V$ would have to be at least as “large” as $W$ in order for every vector in $W$ to be the image of at least one vector in $V$.
I'm having trouble sort of “wrapping up” this proof. Here's what I have.
Let $x in V, y in W$.
We know $x$ must be a linear combination of some basis for $V$. Say that basis is ${v_1, cdots, v_n}$. $x = c_1v_1 + cdots + c_nv_n$.
From the properties of a linear transformation we know that: $T(x) = c_1T(v_1) + cdots + c_nT(v_n) = y$.
This tells me that $y$ is a linear combination of the set ${T(v_1), cdots T(v_n)}$. This means that the some (non-strict) subset of ${T(v_1), cdots, T(v_n)}$ must form a basis for $W$. A basis for $W$ requires at least $m$ linearly independent vectors. Thus, $n geq m$.
My gut says this feels incomplete. I feel like I've made an assumption that the set ${T(v_1), cdots, T(v_n)}$ is linearly independent because we know ${v_1, cdots, v_n}$ is, but I haven't shown that. Any hints?
linear-algebra linear-transformations
Say we have a linear transformation $T : V to W$. We know that $T$ is surjective or “onto $W$”.
I'm trying to prove that $n = dim(V) > m = dim(W)$. Intuitively, this makes sense. $V$ would have to be at least as “large” as $W$ in order for every vector in $W$ to be the image of at least one vector in $V$.
I'm having trouble sort of “wrapping up” this proof. Here's what I have.
Let $x in V, y in W$.
We know $x$ must be a linear combination of some basis for $V$. Say that basis is ${v_1, cdots, v_n}$. $x = c_1v_1 + cdots + c_nv_n$.
From the properties of a linear transformation we know that: $T(x) = c_1T(v_1) + cdots + c_nT(v_n) = y$.
This tells me that $y$ is a linear combination of the set ${T(v_1), cdots T(v_n)}$. This means that the some (non-strict) subset of ${T(v_1), cdots, T(v_n)}$ must form a basis for $W$. A basis for $W$ requires at least $m$ linearly independent vectors. Thus, $n geq m$.
My gut says this feels incomplete. I feel like I've made an assumption that the set ${T(v_1), cdots, T(v_n)}$ is linearly independent because we know ${v_1, cdots, v_n}$ is, but I haven't shown that. Any hints?
linear-algebra linear-transformations
linear-algebra linear-transformations
edited Nov 17 at 20:55
asked Nov 17 at 20:00
Emily Horsman
111110
111110
1
Can you use the rank-nullity formula?
– Bernard
Nov 17 at 20:12
add a comment |
1
Can you use the rank-nullity formula?
– Bernard
Nov 17 at 20:12
1
1
Can you use the rank-nullity formula?
– Bernard
Nov 17 at 20:12
Can you use the rank-nullity formula?
– Bernard
Nov 17 at 20:12
add a comment |
2 Answers
2
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oldest
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up vote
1
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accepted
Your idea is good: if ${v_1,dots,v_n}$ is a basis of $V$, then ${T(v_1),dots,T(v_n)}$ is a spanning set for $W$, which you can extract a basis from. Therefore $dim Wledim V$.
How can you extract a basis? Suppose that ${u_1,dots,u_r}$ is a spanning set of the vector space $U$. Then either the set is linearly dependent or independent. In the latter case you're finished. Otherwise, one vector is a linear combination of the others; without loss of generality, it can be taken as $u_r$ and then ${u_1,dots,u_{r-1}}$ is again a spanning set. Repeat until you have to stop because the set you get is linearly independent.
The rank-nullity theorem tells even more: if $Tcolon Vto W$ is a linear map, then
$$
dim V=dimker T+dimoperatorname{im}T
$$
(where $ker T$ is the kernel and $operatorname{im}T$ is the image). If $T$ is surjective, then $dim W=dim V-dimker Tle dim V$.
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
add a comment |
up vote
1
down vote
I would start from a basis of $W$: $w_1,ldots,w_m$. $T$ is surjective then there are $v_1,ldots,v_m$ such that $w_k=T(v_k)$. It is straigthforward by definition to verify that $v_1,ldots, v_m$ are linearly independent
$$
sum c_kv_k=0implies Tbig(sum c_kv_kbig)=sum c_k w_k=0implies text{ all }c_k=0,
$$
hence, $nge m$.
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
Your idea is good: if ${v_1,dots,v_n}$ is a basis of $V$, then ${T(v_1),dots,T(v_n)}$ is a spanning set for $W$, which you can extract a basis from. Therefore $dim Wledim V$.
How can you extract a basis? Suppose that ${u_1,dots,u_r}$ is a spanning set of the vector space $U$. Then either the set is linearly dependent or independent. In the latter case you're finished. Otherwise, one vector is a linear combination of the others; without loss of generality, it can be taken as $u_r$ and then ${u_1,dots,u_{r-1}}$ is again a spanning set. Repeat until you have to stop because the set you get is linearly independent.
The rank-nullity theorem tells even more: if $Tcolon Vto W$ is a linear map, then
$$
dim V=dimker T+dimoperatorname{im}T
$$
(where $ker T$ is the kernel and $operatorname{im}T$ is the image). If $T$ is surjective, then $dim W=dim V-dimker Tle dim V$.
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
add a comment |
up vote
1
down vote
accepted
Your idea is good: if ${v_1,dots,v_n}$ is a basis of $V$, then ${T(v_1),dots,T(v_n)}$ is a spanning set for $W$, which you can extract a basis from. Therefore $dim Wledim V$.
How can you extract a basis? Suppose that ${u_1,dots,u_r}$ is a spanning set of the vector space $U$. Then either the set is linearly dependent or independent. In the latter case you're finished. Otherwise, one vector is a linear combination of the others; without loss of generality, it can be taken as $u_r$ and then ${u_1,dots,u_{r-1}}$ is again a spanning set. Repeat until you have to stop because the set you get is linearly independent.
The rank-nullity theorem tells even more: if $Tcolon Vto W$ is a linear map, then
$$
dim V=dimker T+dimoperatorname{im}T
$$
(where $ker T$ is the kernel and $operatorname{im}T$ is the image). If $T$ is surjective, then $dim W=dim V-dimker Tle dim V$.
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Your idea is good: if ${v_1,dots,v_n}$ is a basis of $V$, then ${T(v_1),dots,T(v_n)}$ is a spanning set for $W$, which you can extract a basis from. Therefore $dim Wledim V$.
How can you extract a basis? Suppose that ${u_1,dots,u_r}$ is a spanning set of the vector space $U$. Then either the set is linearly dependent or independent. In the latter case you're finished. Otherwise, one vector is a linear combination of the others; without loss of generality, it can be taken as $u_r$ and then ${u_1,dots,u_{r-1}}$ is again a spanning set. Repeat until you have to stop because the set you get is linearly independent.
The rank-nullity theorem tells even more: if $Tcolon Vto W$ is a linear map, then
$$
dim V=dimker T+dimoperatorname{im}T
$$
(where $ker T$ is the kernel and $operatorname{im}T$ is the image). If $T$ is surjective, then $dim W=dim V-dimker Tle dim V$.
Your idea is good: if ${v_1,dots,v_n}$ is a basis of $V$, then ${T(v_1),dots,T(v_n)}$ is a spanning set for $W$, which you can extract a basis from. Therefore $dim Wledim V$.
How can you extract a basis? Suppose that ${u_1,dots,u_r}$ is a spanning set of the vector space $U$. Then either the set is linearly dependent or independent. In the latter case you're finished. Otherwise, one vector is a linear combination of the others; without loss of generality, it can be taken as $u_r$ and then ${u_1,dots,u_{r-1}}$ is again a spanning set. Repeat until you have to stop because the set you get is linearly independent.
The rank-nullity theorem tells even more: if $Tcolon Vto W$ is a linear map, then
$$
dim V=dimker T+dimoperatorname{im}T
$$
(where $ker T$ is the kernel and $operatorname{im}T$ is the image). If $T$ is surjective, then $dim W=dim V-dimker Tle dim V$.
answered Nov 17 at 21:23
egreg
175k1383198
175k1383198
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
add a comment |
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
"If $T$ is surjective, then $dim W = dim V - dim ker T$" Does this imply that $T$ maps any vector in $V$ to either a vector in $W$ or to 0?
– Emily Horsman
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
@EmilyHorsman Isn't $0$ a vector in $W$? Don't read too much in the formula: it's a numeric relation between dimensions.
– egreg
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Ah right, that's true.
– Emily Horsman
Nov 17 at 21:34
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
Okay here's my summary to ensure I understand. $text{rank}(T) = dim(V) - text{nullity}(T)$. $dim(W) = text{rank}(T)$ since $T$ is surjective and thus every vector in $W$ is the image of a vector in $V$. $dim(W) = dim(V) - text{nullity}(T) leq dim(V)$.
– Emily Horsman
Nov 17 at 21:52
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
@EmilyHorsman Right so.
– egreg
Nov 17 at 22:03
add a comment |
up vote
1
down vote
I would start from a basis of $W$: $w_1,ldots,w_m$. $T$ is surjective then there are $v_1,ldots,v_m$ such that $w_k=T(v_k)$. It is straigthforward by definition to verify that $v_1,ldots, v_m$ are linearly independent
$$
sum c_kv_k=0implies Tbig(sum c_kv_kbig)=sum c_k w_k=0implies text{ all }c_k=0,
$$
hence, $nge m$.
add a comment |
up vote
1
down vote
I would start from a basis of $W$: $w_1,ldots,w_m$. $T$ is surjective then there are $v_1,ldots,v_m$ such that $w_k=T(v_k)$. It is straigthforward by definition to verify that $v_1,ldots, v_m$ are linearly independent
$$
sum c_kv_k=0implies Tbig(sum c_kv_kbig)=sum c_k w_k=0implies text{ all }c_k=0,
$$
hence, $nge m$.
add a comment |
up vote
1
down vote
up vote
1
down vote
I would start from a basis of $W$: $w_1,ldots,w_m$. $T$ is surjective then there are $v_1,ldots,v_m$ such that $w_k=T(v_k)$. It is straigthforward by definition to verify that $v_1,ldots, v_m$ are linearly independent
$$
sum c_kv_k=0implies Tbig(sum c_kv_kbig)=sum c_k w_k=0implies text{ all }c_k=0,
$$
hence, $nge m$.
I would start from a basis of $W$: $w_1,ldots,w_m$. $T$ is surjective then there are $v_1,ldots,v_m$ such that $w_k=T(v_k)$. It is straigthforward by definition to verify that $v_1,ldots, v_m$ are linearly independent
$$
sum c_kv_k=0implies Tbig(sum c_kv_kbig)=sum c_k w_k=0implies text{ all }c_k=0,
$$
hence, $nge m$.
answered Nov 17 at 21:34
A.Γ.
21.2k22455
21.2k22455
add a comment |
add a comment |
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1
Can you use the rank-nullity formula?
– Bernard
Nov 17 at 20:12