Can a Linear Transformation Be Onto If the Codomain's Dimension Is Higher?

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Homework Help Overview

The discussion revolves around the properties of linear transformations, specifically addressing whether a linear transformation can be onto when the dimension of the codomain exceeds that of the domain. The original poster is grappling with the implications of dimensionality in linear mappings.

Discussion Character

  • Exploratory, Assumption checking, Conceptual clarification

Approaches and Questions Raised

  • The original poster attempts to understand why a linear transformation cannot be onto if the codomain's dimension is greater than that of the domain. They express confusion about the implications of dimensionality. Other participants question the reasoning and explore the relationship between the dimensions of the involved spaces.

Discussion Status

Participants are actively engaging with the problem, offering hints and exploring the dimensional relationships. Some have suggested considering the basis of the codomain and the implications of the rank-nullity theorem. The discussion is ongoing, with no explicit consensus reached yet.

Contextual Notes

The original poster is specifically focused on parts of a problem that involve proving properties of linear transformations based on dimensionality, indicating a structured homework context. There is a hint of confusion regarding the definitions and implications of onto mappings in relation to the dimensions of the spaces involved.

discoverer02
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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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Why doesn't the same approach work?
 
I'm wondering the same thing, so there must be something that I'm not seeing.

I'll think about it some more.

Thanks.
 
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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Hint: Do you think that T inverse is defined for all of W?
 
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
 

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