Proving a transformation is linear

In summary, the conversation discusses how to show the linearity of T by finding a standard matrix A and proving that the given equation holds. It also mentions the definition of a linear transformation and finding a counter example to prove that T is not linear. The conversation concludes with a simpler approach to the problem by using the fact that T(0)=0 if T is linear.
  • #1
_N3WTON_
351
3

Homework Statement


0%5Cbegin%7Bbmatrix%7D%202x_1%20-%203x_2%5C%5C%202x_2%5C%5C%204x_1%20+%203%20%5Cend%7Bbmatrix%7D.gif

If T is linear, show that it is linear by finding a standard matrix A for T so that:
gif.gif

Also show that this equation holds for the matrix you have found. If T is not linear, prove that T is not linear by showing that it does not fit the definition of a linear transformation

Homework Equations


Definition of a linear transformation:
[itex] T(\vec{u}+\vec{v}) = T(\vec{u})+T(\vec{v}) [/itex]
[itex] T(c\vec{u}) = cT(\vec{u}) [/itex]

The Attempt at a Solution


First I let $$ \vec{e}_{1} =
\begin{bmatrix}
1\\
0
\\0

\end{bmatrix} $$
$$ \vec{e}_{2} =
\begin{bmatrix}
0\\
1
\\0

\end{bmatrix}$$
$$ \vec{e}_{3} =
\begin{bmatrix}
0\\
0
\\1

\end{bmatrix}$$
However, when I go to separate
$$
\vec{b} = \begin{bmatrix}
2x_1 - 3x_2\\
2x_2\\
4x_1 + 3
\end{bmatrix}$$ I am not sure how to handle the constant, i.e, I am not sure how to rewrite as [itex] A\vec{x} [/itex]. I think once I figure that out I should be able to do the rest of problem
 
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  • #2
It is not linear ... Just find a counter example to the definitions of what a linear transformation is.
 
  • #3
(Edit : deleted, duplicate response)
 
Last edited:
  • #4
Orodruin said:
It is not linear ... Just find a counter example to the definitions of what a linear transformation is.
Ok, I didn't realize that was all I needed to do. I have attached a picture of my work because it would be kind of long to write out in latex:
hsPShzu.jpg
 
  • #5
You could simplify that a lot. If T is linear, then T(0)=0, since T(0x)=0T(x). Is it for your given T?
 
  • #6
Dick said:
You could simplify that a lot. If T is linear, then T(0)=0, since T(0x)=0T(x). Is it for your given T?
wow I hadn't thought of that, sort of makes the problem trivial lol...anyway thanks for the help :)
 

Related to Proving a transformation is linear

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another in a way that preserves the structure of the original space. In simpler terms, it is a function that takes in a vector and outputs another vector in a straight line, without any bending or curving.

2. How can you prove that a transformation is linear?

To prove that a transformation is linear, you need to show that it satisfies two key properties: additivity and homogeneity. Additivity means that the transformation of the sum of two vectors is equal to the sum of the individual transformations. Homogeneity means that the transformation of a scalar multiple of a vector is equal to the scalar multiple of the transformation of the original vector.

3. What are some common methods for proving linearity?

There are several methods for proving linearity, including verifying the additivity and homogeneity properties, using matrix representations, and checking if the transformation preserves basic geometric properties such as collinearity and parallelism. Another common method is to use the definition of linearity to show that the transformation satisfies the required conditions.

4. Can a non-linear transformation ever be considered linear?

No, by definition, a non-linear transformation cannot be considered linear. A transformation must satisfy the properties of additivity and homogeneity to be considered linear, and a non-linear transformation does not satisfy these properties.

5. Why is it important to prove that a transformation is linear?

Proving that a transformation is linear is important because it ensures that the transformation preserves the structure of the original vector space. This is crucial in many mathematical and scientific applications, as it allows us to accurately analyze and manipulate data without introducing any distortions or errors.

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