Question regarding linear transformation

Click For Summary
SUMMARY

The discussion centers on proving that a linear transformation T maps \(\mathbb{R}^3\) to \(\mathbb{R}^2\) using the standard matrix A = \(\begin{bmatrix} 1 & 0 & 4 \\ 2 & 1 & 6 \end{bmatrix}\). The theorem referenced states that T is onto if the span of the columns of A equals \(\mathbb{R}^2\). To demonstrate this, one must show that any vector in \(\mathbb{R}^2\) can be expressed as a linear combination of the columns of A, which can be verified by row-reducing A and confirming that the second row does not reduce to all zeros.

PREREQUISITES
  • Understanding of linear transformations and their properties
  • Familiarity with matrix representation of linear transformations
  • Knowledge of vector spaces and spans
  • Ability to perform row reduction on matrices
NEXT STEPS
  • Learn about the concept of span in vector spaces
  • Study row reduction techniques for matrices
  • Explore the implications of linear independence in linear transformations
  • Investigate the relationship between linear transformations and their standard matrices
USEFUL FOR

Students and educators in linear algebra, mathematicians focusing on vector spaces, and anyone interested in understanding linear transformations and their applications in higher mathematics.

Mathman23
Messages
248
Reaction score
0
Hi

I have a linear transformation T which maps \mathbb{R}^3 \rightarrow \mathbb{R}^2 a A is the standard matrix for the linear transformation.

I'm suppose to determain that T maps \mathbb{R}^3 \rightarrow \mathbb{R}^2

I was told by my professor about the following theorem.

If T:\mathbb{R}^n \rightarrow \mathbb{R}^m is a linear transformation and A is the standard matrix for T. Then

a/ T maps \mathbb{R}^n \rightarrow \mathbb{R}^m if and only if A \mathrm{span} \{ \mathbb{R}^m \}

b/ T is one-to-one if and only if columns of A are linearly independent.

If I then apply (a) from to my problem:

A = \left[ \begin{array}{ccc} 1 & 0 & 4 \\ 2 & 1 & 6\\ \end{array} \right ]

A being the standard matrix of the linear transformation.

A can also be written:

\left[ \begin{array}{cc} 1 \\2 \end{array} \right ] \mathrm{x} + \left[ \begin{array}{cc} 0 \\1 \end{array} \right ] \mathrm{y} + \left[ \begin{array}{cc} 4 \\6 \end{array} \right ] \mathrm{z} = \left[ \begin{array}{cc} 0 \\0 \end{array} \right ]

This is equal to:


\begin{equation}\nonumber<br /> x + 4z = 0<br /> \end{equation}

\begin{equation}\nonumber<br /> 2x + y + 6z = 0<br /> \end{equation}

x in equation 1 can be written as x = -4z

If I insert that x into equation 2 then I get

-2z + y = 0

But what do I then do to prove point (a) in the theorem ?

Sincerley and many thanks in advance

Fred
 
Physics news on Phys.org
Actually this: "I have a linear transformation T which maps \mathbb{R}^3 \rightarrow \mathbb{R}^2
a A is the standard matrix for the linear transformation.

I'm suppose to determain that T maps \mathbb{R}^3 \rightarrow \mathbb{R}^2" doesn't quite make sense! I assume you mean simply that T is from\mathbb{R}^3to \mathbb{R}^2 and you want to prove this is an "onto" mapping.
Okay, you are given A= A = \left[ \begin{array}{ccc} 1 &amp; 0 &amp; 4 \\ 2 &amp; 1 &amp; 6\\ \end{array} \right ]

I don't understand why you then set Ax= 0: that would prove that the columns are linearly independent which is your professor's (b), not (a) (and this is clearly not one to one). To show that A is "onto", you need to show that for all y in \mathbb{R}^2,there exist x in \mathbb{R}^3 so that Ax= y. The simplest way to do that is to row-reduce A. If the second row does not reduce to all 0s, it's true.
 


Hi Fred,

To prove point (a) in the theorem, you need to show that the span of the columns of A is equal to \mathbb{R}^m. In other words, you need to show that any vector in \mathbb{R}^m can be written as a linear combination of the columns of A.

In your case, since A = \left[ \begin{array}{ccc} 1 & 0 & 4 \\ 2 & 1 & 6\\ \end{array} \right ], the span of the columns of A will be all possible linear combinations of the three columns. This means that any vector in \mathbb{R}^2 can be written as a linear combination of the columns of A. For example, the vector \left[ \begin{array}{cc} 2 \\ 3 \end{array} \right ] can be written as 2\left[ \begin{array}{cc} 1 \\ 2 \end{array} \right ] + 3\left[ \begin{array}{cc} 0 \\ 1 \end{array} \right ] + 0\left[ \begin{array}{cc} 4 \\ 6 \end{array} \right ]. This shows that the span of the columns of A is equal to \mathbb{R}^2, and therefore, T maps \mathbb{R}^3 \rightarrow \mathbb{R}^2.

I hope this helps. Let me know if you have any other questions.

 

Similar threads

  • · Replies 19 ·
Replies
19
Views
3K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 3 ·
Replies
3
Views
1K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 1 ·
Replies
1
Views
4K
  • · Replies 52 ·
2
Replies
52
Views
4K
  • · Replies 23 ·
Replies
23
Views
2K
  • · Replies 4 ·
Replies
4
Views
2K