Proving a Linear Transformation is Onto

In summary, the conversation discusses the theorem that a linear map T: V→W is one-to-one if and only if Ker(T) = 0, and whether there is an analog for showing that T is onto. The participants also mention the definition of "onto" for linear transformations, the "range-nullity" theorem, and the conditions for a linear map to be an isomorphism.
  • #1
BrainHurts
102
0
There's this theorem:

A linear map T: V→W is one-to-one iff Ker(T) = 0

I'm wondering if there's an analog for showing that T is onto? If so could you provide a proof?

I'm thinking it has something to do with the rank(T)...
 
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  • #2
The definition of "onto" is b
"f: U-> V is said to be "onto" V if and only if f(U)= V"

Now, for linear transformations, f(U) is always a subspace of V so f is "onto" V if and only if range(f)= dim(V).

Do you know that "range-nullity" theorem? For linear transformation f:U-> V nullity(f)+ range(f)= dim(V). In particular, if f is one-to-one, so that nullity(f)= 0, we must have range(f)= dim(V) so that f is also "onto" V. Conversely, if f is "onto" V, then it is also one-to-one. That is, a linear transformation is "one-to-one" if and only if it is "onto".
 
  • #3
Halls I believe you left something out. A linear map [itex]T:U\rightarrow V[/itex] is injective if and only if it is surjective provided that [itex]dimU = dimV[/itex]. In particular, as you said if [itex]T[/itex] is injective then [itex]nullity(T) = 0[/itex] thus [itex]rank(T) = dim(T(U)) = dimU[/itex] by rank nullity but we then need that [itex]dimU = dimV[/itex] because [itex]dim(T(U)) = dimV,T(U)\subseteq V\Rightarrow T(U) = V[/itex] and similarly for the converse. I believe you had a typo because it should be [itex]rank(T) + nullity(T) = dimU[/itex] as opposed to [itex]dimV[/itex].
 
  • #4
Yes, thank you for the correction.
 
  • #5
HallsofIvy, just to be sure, you mean that rank(f) because range(f) is a set, and if f is onto then range(f) = V


I am familiar with the rank-nullity theorem. Let f:U-->V is a linear map, rank(f) + nullity(f) = dimU. I think that's what you meant because I am familiar with the statement that T (A linear map) is 1-1 iff nullity(T) = 0

WannabeNewton: You guys both made a similar statement that a linear map T: U-->V is 1-1 iff range(T) = V and that dim(U)=dim(V)

So T is an isomorphism if dim(U)=dim(V) and if T is either 1-1 or onto?
 
  • #7
WannabeNewton said:
Halls I believe you left something out. A linear map [itex]T:U\rightarrow V[/itex] is injective if and only if it is surjective provided that [itex]dimU = dimV[/itex]. In particular, as you said if [itex]T[/itex] is injective then [itex]nullity(T) = 0[/itex] thus [itex]rank(T) = dim(T(U)) = dimU[/itex] by rank nullity but we then need that [itex]dimU = dimV[/itex] because [itex]dim(T(U)) = dimV,T(U)\subseteq V\Rightarrow T(U) = V[/itex] and similarly for the converse. I believe you had a typo because it should be [itex]rank(T) + nullity(T) = dimU[/itex] as opposed to [itex]dimV[/itex].
Just a little LaTeX tip: Use something like \operatorname or \mathrm to get ##\operatorname{dim}V## instead of ##dim V##.
 
  • #8
Fredrik said:
Just a little LaTeX tip: Use something like \operatorname or \mathrm to get ##\operatorname{dim}V## instead of ##dim V##.
Thanks broski =D
 
  • #9
BrainHurts said:
HallsofIvy, just to be sure, you mean that rank(f) because range(f) is a set, and if f is onto then range(f) = V
Yes, not one of my better days is it!

I am familiar with the rank-nullity theorem. Let f:U-->V is a linear map, rank(f) + nullity(f) = dimU. I think that's what you meant because I am familiar with the statement that T (A linear map) is 1-1 iff nullity(T) = 0

WannabeNewton: You guys both made a similar statement that a linear map T: U-->V is 1-1 iff range(T) = V and that dim(U)=dim(V)

So T is an isomorphism if dim(U)=dim(V) and if T is either 1-1 or onto?
 

1. What does it mean for a linear transformation to be onto?

A linear transformation is onto if every element in the range of the transformation has a corresponding element in the domain. In other words, every output in the range is mapped from at least one input in the domain.

2. How do you prove that a linear transformation is onto?

To prove that a linear transformation is onto, you must show that every element in the range has a corresponding element in the domain. This can be done by showing that every element in the range can be obtained by applying the transformation to an element in the domain.

3. Can a linear transformation be both onto and one-to-one?

Yes, a linear transformation can be both onto and one-to-one. This is known as a bijective linear transformation, and it means that every element in the range has a unique corresponding element in the domain.

4. What is the difference between being onto and being surjective?

There is no difference between being onto and being surjective. They both mean that every element in the range has a corresponding element in the domain. However, the term "onto" is more commonly used in linear algebra, while "surjective" is more commonly used in general mathematics.

5. Can a linear transformation be onto if the domain and range have different dimensions?

Yes, a linear transformation can still be onto even if the domain and range have different dimensions. This means that not all elements in the domain will have a corresponding element in the range, but every element in the range will still have at least one corresponding element in the domain.

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