Onto linear transformation

In summary, the conversation discusses the concepts of one-to-one and onto linear transformations and how they can be affected by changing the domain and range of a function. The matrix presented is found to be not one-to-one, but can still be potentially onto depending on the chosen range. The importance of carefully defining the domain and range is emphasized.
  • #1
Arnoldjavs3
191
3

Homework Statement


Say I have a matrix:

[3 -2 1]
[1 -4 1]
[1 1 0]

Is this matrix onto? One to one?

Homework Equations

The Attempt at a Solution


I know it's not one to one. In ker(T) there are non trivial solutions to the system. But since I've confirmed there is something in the ker(T), does this indicate that it is also not onto as well? I know that being an onto transformation is the Im(T) where it represents all transformed vectors.

The reduced matrix I got was this:
[ 1 0 1/5 | c-b/5]
[0 1 -1/5 | b/5]
[0 0 0 | a - b ]

Can 0 = a-b ever? If I put in random values for a,b,c the system will be inconsistent usually.
 
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  • #2
You should be aware that a linear transformation ##T: V \rightarrow V## is one-to-one if and only if it is onto.
 
  • #3
JonnyG said:
You should be aware that a linear transformation ##T: V \rightarrow V## is one-to-one if and only if it is onto.

Let's assume that it was from R4 -> R3. What now? It should never be one-to-one in that case, but can it still be onto?
 
  • #4
Yes it can. Take for example, ##T(x,y,z,w) = T(x,y,z,0)##.

But in any case, your transformation is from ##\mathbb{R}^3## to ##\mathbb{R}^3##
 
  • #5
Arnoldjavs3 said:
Let's assume that it was from R4 -> R3. What now? It should never be one-to-one in that case, but can it still be onto?

Every function is onto its image, as by definition the "image" is precisely the set mapped to by the function. You have to careful, therefore, when talking about whether a function is onto. Onto refers to whether the function maps to all the "range" of the function. So, you can change a function from onto to not onto or vice versa, simply by changing the nominated range.

A linear transformation from a vector space into itself is onto iff it is one-to-one. But, if you change the nominated range, this is no longer true.

You could, therefore, have phrased your question better. You could have asked:

Say I have a matrix:

[3 -2 1]
[1 -4 1]
[1 1 0]

Does this matrix represent an onto mapping from ##\mathbb{R}^3## to iself?

Note that the property of being "one to one" can also be gained or lost by changing the domain of the function. Most often this is used, for example with trig functions, by restricting a function to a smaller domain to make it one-to-one and hence to have an inverse on that domain.
 

1. What is an "onto" linear transformation?

An "onto" linear transformation is a type of mathematical function that maps elements from one vector space to another in a way that every element in the target space is mapped to by at least one element in the original space. This means that the function covers the entire target space, or is "onto" it.

2. How is an "onto" linear transformation represented?

An "onto" linear transformation is typically represented as a matrix, where the columns represent the basis vectors of the original space and the rows represent the basis vectors of the target space. The values in the matrix correspond to the coefficients used to transform the original space to the target space.

3. What are some real-world applications of "onto" linear transformations?

"Onto" linear transformations are used in various fields, including engineering, physics, and computer science. Examples include image and signal processing, data compression, and cryptography. They are also used in the study of dynamical systems, where they can help model complex systems with multiple variables and interactions.

4. How do you determine if a linear transformation is "onto"?

To determine if a linear transformation is "onto", you can check if the matrix representing the transformation has a pivot in every row. If it does, then the transformation is "onto" as it covers the entire target space. Another way to check is to see if the rank of the matrix is equal to the dimension of the target space.

5. Can an "onto" linear transformation also be "one-to-one"?

No, an "onto" linear transformation is not necessarily "one-to-one". A "one-to-one" transformation is a function where each element in the original space maps to a unique element in the target space. This means that the function does not map multiple elements to the same element. However, an "onto" linear transformation can map multiple elements to the same element in the target space.

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