Linear Transformation questions about dimensions

In summary, the conversation discusses how to show that a linear transform from a higher dimension space to a lower dimension space has a kernel with more than one element, and how to prove that a transform from a lower dimension space to a higher dimension space is not surjective. This can be done by using the theorem that states the dimension of the domain is equal to the sum of the dimensions of the kernel and image. The conversation also includes a lighthearted reference to the "Rank-Nullity Theorem" and jokingly mentions bad math jokes related to it.
  • #1
potmobius
49
0
1. Say you have a linear transform from A to B, and where A has a higher dimension than B. How do you show that the kernel of the transform has more than one element (i.e. 0)? Also, if B has a higher dimension than A, then how to show that the transform isn't surjective?

2. The attempt at a solution

By showing that the kernel has more than the element 0, I want to show that the transform isn't injective. But I'm not quite sure how to get there just by using the fact that A has a higher dimension than B. Is that a good way(as in, not too complicated) of proving it? Any ideas?

For the other part, it makes sense intuitively, since the basis of A will have less elements than the basis of B, so there shouldn't be a surjection. But how do you proceed from there to show that the image of A is a proper subset of B?
 
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  • #2
Think of the theorem that says the dim(A)=dim(Ker(f)) + dim(Im(f)), where f:A->B is a linear morphism (transformation)
 
  • #3
Thanks! Now I feel really stupid for not considering Rank-Nullity before asking this...
 
  • #4
potmobius said:
Thanks! Now I feel really stupid for not considering Rank-Nullity before asking this...

You're welcome. I feel stupid for not knowing that theorem has a name ...
 
  • #5
Do you know any bad math jokes related to this?
 

What is a linear transformation?

A linear transformation is a mathematical function that maps a vector from one vector space to another vector space, while preserving the basic structure of the vector space. This means that the transformation must preserve addition and scalar multiplication, and the origin must remain fixed.

How does a linear transformation affect the dimensions of a vector space?

A linear transformation can change the dimensions of a vector space. For example, a transformation may map a 3-dimensional vector space to a 2-dimensional vector space. However, the transformation must still preserve the basic structure of the vector space, as mentioned before.

Can a linear transformation change the basis of a vector space?

Yes, a linear transformation can change the basis of a vector space. In fact, the transformation can change the basis of both the domain and the codomain vector space. However, the transformation must still preserve the basic structure of the vector space.

What is the difference between a linear transformation and a non-linear transformation?

A linear transformation preserves the basic structure of a vector space, while a non-linear transformation does not. This means that a non-linear transformation may change the shape of the vector space, and it does not necessarily have a fixed origin. Additionally, a linear transformation can be represented by a matrix, while a non-linear transformation cannot.

What are some real-world applications of linear transformations?

Linear transformations have many applications in fields such as physics, engineering, and computer graphics. For example, they can be used to represent and manipulate objects in a 3D space, or to solve systems of linear equations in physics and engineering problems. They are also used in machine learning and data analysis to transform and analyze data in different dimensions.

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