Proof of a linear transformation not being onto

In summary: Assume that $T$ is onto and then arrive at a contradiction. Therefore, our assumption that $T$ is onto must be false, meaning that $T$ is not onto.
  • #1
baseball3030
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0
proof onto

Prove: A linear Map T:Rn->Rm is an onto function :
The only way I have thought about doing this problem is by proving the contrapositive:
 
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  • #2
baseball3030 said:
Prove: A linear Map T:Rn->Rm is not onto if m>n.
The only way I have thought about doing this problem is by proving the contrapositive:

If m<=n then T:Rn->Rm is onto.

I would start by letting there be a transformation
matrix with dimension mxn.

Then the only thing I can think of doing is using the rank nullity thm to show that the dimension of the range=dimension of v. Does anyone know of any other ways to go about this or if my way would be correct? Thank you so much

The counterpositive is "If T is onto, then m<=n" It's easier to do it by the theorem that says that the dimensions of the kernel and of the image of T add to n.
 
  • #3
You can do a direct proof, and I do not think one needs to invoke rank-nullity.

let $B = \{v_1,\dots,v_n\}$ be a basis for $\Bbb R^n$.

Consider the set $T(B) \subset \Bbb R^m$.

If $u \in T(\Bbb R^n) = \text{im}(T)$ we have:

$u = T(x)$ for some $x = c_1v_1 + \cdots + c_nv_n \in \Bbb R^n$.

Thus:

$u = c_1T(v_1) + \cdots c_nT(v_n)$, which shows $T(B)$ spans $\text{im}(T)$.

Since $|T(B)| \leq n < m$, we have that the dimension of $\text{im}(T) \leq n < m$.

However, if $\text{im}(T) = \Bbb R^m$, we have that $T(B)$ spans $\Bbb R^m$ leading to:

$m \leq |T(B)| \leq n < m$, a contradiction.

As a general rule, functions can, at best, only "preserve" the "size" of their domain, they cannot enlarge it. Dimension, for vector spaces, is one way of measuring "size".

While it is technically possible to have a function from $\Bbb R^n \to \Bbb R^m$ where $m > n$ (so called "space-filling functions") that is onto, such functions turn out to be rather bizarre and cannot be linear (they do not preserve linear combinations). Linear maps cannot "grow in dimension", for the same reason the column rank cannot exceed the number of rows in a matrix (even if we have more columns than rows).
 
  • #4
How are you able to say that
Since |T(B)|≤n<m , we have that the dimension of im(T)≤n<m ?

I understand the T(B)≤n but don't know how you know that n<m?

I understand everything up to that point. Thank you very much, I appreciate it.
 
  • #5
$n < m$ is given by the problem, yes?
 
  • #6
Yeah but the problem states that the linear map is not onto if m<n so are you assuming that it is onto and then arriving at a contradiction? Thanks,
 
  • #7
Yes, I am doing a proof by contradiction (or reductio ad absurdum).
 

FAQ: Proof of a linear transformation not being onto

What is a linear transformation?

A linear transformation is a function that maps one vector space to another, and preserves the operations of vector addition and scalar multiplication. It can also be understood as a transformation that preserves lines and origin in a vector space.

What does it mean for a linear transformation to not be onto?

If a linear transformation is not onto, it means that the transformation does not map every element in the domain to an element in the codomain. In other words, there are some elements in the codomain that are not the image of any element in the domain under the transformation.

How can I prove that a linear transformation is not onto?

To prove that a linear transformation is not onto, you can either find a specific element in the codomain that is not the image of any element in the domain, or show that the dimension of the image is less than the dimension of the codomain. Another way is to use the rank-nullity theorem, which states that the dimension of the kernel (nullity) plus the dimension of the image (rank) equals the dimension of the domain.

What are some examples of linear transformations that are not onto?

One example is the transformation that takes a 2-dimensional vector to a 3-dimensional vector, by adding a third component of 0. Another example is the transformation that maps all vectors to the zero vector. Any transformation that reduces the dimension of the vector space will also not be onto.

Why is it important to prove that a linear transformation is not onto?

Proving that a linear transformation is not onto is important because it helps us understand the behavior of the transformation and its limitations. It also allows us to determine if the transformation is invertible, which can have important implications in various mathematical applications. Additionally, it can give us insights into the structure of the vector space and the relationship between the domain and codomain of the transformation.

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