Showing a transformation is not linear

In summary, a linear transformation is a mathematical function that preserves the linear structure of a vector space. To show that a transformation is not linear, a counterexample can be provided. The main properties of a linear transformation are additivity, homogeneity, and preservation of the origin. A transformation cannot be partially linear, as it either satisfies all the properties or it is not considered linear. The term "linear" is often associated with straight lines, but it actually refers to a set of properties that can also apply to curved lines and shapes in higher dimensions.
  • #1
TranscendArcu
285
0

Homework Statement



http://img526.imageshack.us/img526/4926/screenshot20120128at941.png

The Attempt at a Solution


A linear transformation must satisfy the property: [itex]T(a \vec{X} ) = a T(\vec{X} )[/itex] [itex] \forall X \in V, a \in R[/itex]. However, it is not in general true that [itex](a^2 x^2,a^2 y^2 = a(x^2,y^2)[/itex]. In particular, we can see this fails for [itex]a = 3, x= 4,y=5[/itex]. Indeed,

[itex](3^2 4^2,3^2 5^2) ≠ 3(4^2,5^2)[/itex]
[itex](9 \ast 16,9 \ast 25) ≠ 3(16,25)[/itex]
[itex](144,225) ≠ (48,75)[/itex]

Thus, this is not a linear transformation.

Am I doing this right?
 
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  • #3
Good to hear I did the first one right. Thanks! There are four of these problems in total and I want to make sure I know how to do them correctly.

http://img267.imageshack.us/img267/844/screenshot20120128at956.png

A linear transformation must satisfy the property that [itex]T(\vec{A} + \vec{B}) = T(\vec{A}) + T(\vec{B})[/itex]. Let two vectors in [itex]R^3[/itex] be denoted by [itex](x_1,y_1,z_1),(x_2,y_2,z_2)[/itex]. Thus,

[itex]T((x_1,y_1,z_1) + (x_2,y_2,z_2)) = T(x_1 +x_2,y_1 + y_2, z_1 + z_2)[/itex]
[itex]T(x_1 +x_2,y_1 + y_2, z_1 + z_2) = (x_1 +x_2+y_1 + y_2+z_1 + z_2,1)[/itex]

But, [itex]T(x_1,y_1,z_1) + T(x_2,y_2,z_2) = (x_1+y_1+z_1,1) + (x_2+y_2+z_2,1)[/itex]
[itex](x_1+y_1+z_1,1) + (x_2+y_2+z_2,1) = (x_1 +x_2+y_1 + y_2+z_1 + z_2,2)[/itex]

However, [itex](x_1 +x_2+y_1 + y_2+z_1 + z_2,1) ≠ (x_1 +x_2+y_1 + y_2+z_1 + z_2,2)[/itex]. Thus, this is not a linear transformation.
 
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  • #5
http://img192.imageshack.us/img192/7066/screenshot20120128at100.png

We said earlier that a linear transformation has the property: [itex]T(a \vec{X} ) = a T(\vec{X})[/itex]. In this problem,

[itex]T(a x) = (1,-1)[/itex]. However, [itex]a T(x) = a (1,-1) = (a,-a)[/itex].

The equation [itex](1,-1) = (a,-a)[/itex] holds only in the case [itex]a =1[/itex]. Because the equation does not hold for any other number, this is not a linear transformation.
 
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  • #7
http://img85.imageshack.us/img85/6540/screenshot20120128at101.png
We'll use the same property as in the first and third problems to show this is not a linear transformation.

[itex]T[a(x,y)] = T(ax,ay) = (a^2 x y,ay,ax)[/itex]. However, [itex]aT(x,y) = a(xy,y,x) = (axy,ay,ax)[/itex]. Again, we observe that [itex](a^2 x y,ay,ax) = (axy,ay,ax)[/itex] will be true only in two particular cases. These are [itex]a = 1,a= 0[/itex]. Because the equation is not true in general, we conclude this is not a linear transformation.
 
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  • #8
Good! (maybe it's also good to find explicit values of a,x and y such that the equation doesn't hold)
 
  • #9
Okay. I'll add a specific case to show that the equation does not hold. Thanks! My other homework problems are a little bit different, but I'd like to talk about this one in particular:

http://img220.imageshack.us/img220/7180/screenshot20120128at104.png

First, I considered the idea of multiplication as a binary operator. That is, for well-chosen [itex]a_1,a_2,a_3 \in R[/itex] it is conceivable that [itex]a_1 * a_2 = a_3[/itex]. That is, I am able to input two numbers and get one number as a result. With regard to this question, I said let [itex]T(x) = y[/itex] where [itex]y \in R[/itex]. Thus, using the idea of multiplication as a binary operator, I would have [itex]x * t = y[/itex]. In the cases that [itex]x ≠ 0[/itex] I can solve to find [itex]t = \frac{y}{x} \in R[/itex]. In the case [itex] x=0[/itex], we know that any linear transformation of the zero vector of the domain must map to the zero vector of the codomain. In this case, [itex]t[/itex] can be any such number because [itex]t * \vec{0} = \vec {0}[/itex].

First of all, is this a proof? It seems conceivable to me that this could work but I'm not entirely sure.
 
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  • #10
Hmmm, I don't quite understand the proof :( So I guess it's not correct.

Here's a way to prove it: let T(1)=t. Can you now use linearity to show that T(x)=tx??
 
  • #11
micromass said:
Hmmm, I don't quite understand the proof :( So I guess it's not correct.
:,(
Here's a way to prove it: let T(1)=t. Can you now use linearity to show that T(x)=tx??
Alright, letting [itex]T(1) = t[/itex]. We know that [itex]x \in R[/itex]. Thus, we may treat [itex]x[/itex] as a kind of scalar. We can write,

[itex]T(x) = T(1*x) = xT(1) = xt[/itex]. Thus, the existence of such a [itex]t[/itex] is shown.
 
  • #12
TranscendArcu said:
:,(
Alright, letting [itex]T(1) = t[/itex]. We know that [itex]x \in R[/itex]. Thus, we may treat [itex]x[/itex] as a kind of scalar. We can write,

[itex]T(x) = T(1*x) = xT(1) = xt[/itex]. Thus, the existence of such a [itex]t[/itex] is shown.

Tht's good!
 
  • #13
Okay. That's much shorter than what I had previously, so that's an improvement (and it's right, too!). Below is another problem that I'd like my work checked on.

http://img337.imageshack.us/img337/1043/screenshot20120129at101.png

Let [itex]\left\{ A_1,...,A_n \right\}[/itex] be a basis for V, and [itex]\left\{ B_1,...,B_n \right\}[/itex] be a basis for W. If [itex]T[/itex] is injective, then [itex]T(A_i) ≠ T(A_k)[/itex] where [itex]1≤i,k≤n[/itex]. If [itex]T[/itex] is injective, then [itex]ker(T) = \left\{ 0 \right\} [/itex]. We know, [itex]dim(V) = dim(ker(T)) + dim(Im(T))[/itex]. We write [itex]n = 0 + dim(Im(T))[/itex]. This implies [itex]dim(Im(T)) = n [/itex], and thus, [itex]Im(T) = V[/itex], which means [itex]T[/itex] is surjective.

Suppose [itex]T[/itex] is surjective. Then [itex]Im(T) = V[/itex]. Thus, [itex]dim(Im(T)) = dim(V)[/itex]. We write, [itex]dim(Im(T)) = n[/itex]. We know, [itex]dim(V) = dim(ker(T)) + dim(Im(T))[/itex]. We write, [itex]n = dim(ker(T)) + n[/itex]. This implies [itex]dim(ker(T)) = 0[/itex], and thus [itex]ker(T) = \left\{ 0 \right\} [/itex]. This means that [itex]T[/itex] is injective.
 
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  • #14
Seems ok!
 

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 linear structure of the original vector space. In simpler terms, it is a transformation that preserves lines and the origin.

2. How do you show that a transformation is not linear?

To show that a transformation is not linear, you can use the definition of linearity and provide a counterexample. This means finding two vectors in the original vector space such that the transformation of their sum is not equal to the sum of their individual transformations. This violates the property of additivity, which is a key characteristic of linear transformations.

3. What are the main properties of a linear transformation?

The main properties of a linear transformation are additivity, homogeneity, and preservation of the origin. Additivity means that the transformation of the sum of two vectors is equal to the sum of their individual transformations. Homogeneity means that scaling a vector before or after the transformation results in the same final vector. Preservation of the origin means that the transformation of the zero vector is equal to the zero vector.

4. Can a transformation be partially linear?

No, a transformation cannot be partially linear. It either satisfies all the properties of linearity or it does not. If a transformation fails to satisfy even one of the properties, it is not considered linear.

5. How is linearity related to the concept of a straight line?

The term "linear" is often associated with the concept of a straight line, but this can be misleading. In mathematics, linearity refers to a specific set of properties that a transformation must satisfy. These properties are not limited to just straight lines, but can also apply to curved lines and other shapes in higher dimensions.

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