Linear Transformation - Onto

In summary: Let T be a linear transformation from an n-dimensional space V into an m-dimensional space W.If m>n, show that T cannot be a mapping from V onto W.If m<n, show that T cannot be one-to-one.Part b) I can see. I think. T(v) = Av = w The matrix A will have more columns than rows (more unknowns than equations), so there will be infinitely solutions (more than one mapping from a v in V to a w in W).
  • #1
discoverer02
138
1
Linear Transformation -- Onto

I'm having trouble with the first part of the following problem:

Let T be a linear transformation from an n-dimensional space V into an m-dimensional space W.

a) If m>n, show that T cannot be a mapping from V onto W.

b) if m<n, show that T cannot be one-to-one.

Part b) I can see. I think. T(v) = Av = w The matrix A will have more columns than rows (more unknowns than equations), so there will be infinitely solutions (more than one mapping from a v in V to a w in W).

I'm stumped by part a). I'm not seeing how m>n guarantees that there are w 's in W that aren't part of R(T).

A nudge in the right direction would be greatly appreciated.

Thanks.
 
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  • #2
Why doesn't the same approach work?
 
  • #3
I'm wondering the same thing, so there must be something that I'm not seeing.

I'll think about it some more.

Thanks.
 
  • #4
Well, for a linear map: [tex]T:V^n \rightarrow W^m[/tex] where [tex]n<m[/tex]

There is a useful formula which describes subspaces of V and W in terms of conditions they satisfy with respect to T. Then have a look at the dimension of these subspaces, the dimension of V, and the dimension of W. You should be able to establish that there are certain elements in W that aren't the image of any element in V. Hint: Consider a basis of W.
 
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  • #5
Hint: Do you think that T inverse is defined for all of W?
 
  • #6
OK, let's see what I've got so far:

A basis of W would consist of 3 elements, A basis of V would consist of 2 elements.

T(x + y ) = T(x ) + T(y )
T(kx ) = kT(x )

I also have the equation dim(ker(T)) + dim(R(T)) = dim(R2) = 2

Anyway you look at it dim(R(T)) <= 2. This means that any other element in R(T) is a linear combination of these two elements. But W has a basis of 3 elements meaning that R(T) could not possibly contain at least one of W's basis elements.

I think this makes sense finally. I'll think about it some more just to make sure it's solid.

Thanks very much for the hints.

discoverer02
 

What is a linear transformation onto?

A linear transformation onto is a mathematical function that maps every element in one vector space to every element in another vector space. This means that for every output in the target vector space, there is at least one input in the original vector space that produces it.

How is a linear transformation onto different from a linear transformation?

A linear transformation onto is a type of linear transformation that is surjective, meaning that it covers the entire target vector space. In contrast, a linear transformation may not necessarily cover the entire target vector space, and may only map a subset of inputs to outputs.

How can I determine if a linear transformation is onto?

To determine if a linear transformation is onto, you can use the rank-nullity theorem. This theorem states that the rank (dimension of the output space) plus the nullity (dimension of the kernel or set of inputs that result in the zero output) must equal the dimension of the input space. If the rank of the transformation is equal to the dimension of the output space, then the transformation is onto.

Can a linear transformation onto be invertible?

Yes, a linear transformation onto can be invertible. This means that there exists an inverse transformation that maps the output space back to the original input space. However, not all linear transformations onto are invertible, as the inverse transformation must also be linear.

How is a linear transformation onto represented?

A linear transformation onto is typically represented using a matrix. The columns of the matrix represent the basis vectors of the output space, while the rows represent the coordinates of the input vectors. The transformation can then be applied by multiplying the input vector by the matrix.

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