Help understanding what is/is not a linear transformation from R2->R3

In summary, the conversation discusses linear transformations from R2 into R3 and how to determine if they are valid transformations. The three conditions for a linear transformation are stated as 1. L(a*x1+b*x2)=aL(x1)+bL(x2); 2. L(x1+x2)=L(x1)+L(x2); 3. L(a*x1)=aL(x1). The conversation also clarifies the difference between using x1 and x2 as vectors versus coordinates of a vector. The problem at hand is determining if the four given transformations are valid, and it is determined that b and c satisfy all three conditions while a does not because of the difference in notation and d does not because it
  • #1
Daren H
5
0

Homework Statement


Determine whether the following are linear transformations from R2 into R3:

Homework Equations


a) L(x)=(x1, x2, 1)^t
b) L(x)=(x1, x2, x1+2x2)^t
c) L(x)=(x1, 0, 0)^t
d) L(x)=(x1, x2, x1^2+x2^2)^t

The Attempt at a Solution


To show L is a linear transformation, I need to be able to show:
1. L(a*x1+b*x2)=aL(x1)+bL(x2);
2. L(x1+x2)=L(x1)+L(x2);
3. L(a*x1)=aL(x1);

By looking and playing around with this, I can see how d is not a transformation, since if I let a=-1 and b=-2, then rule 3 does not hold. If I'm wrong, please correct me.
But as far as a, b, and c, it looks like all three rules hold. The appendix says a is not a transformation, but I'm not sure why. It seems to me that a also satisfies the 3 conditions, what am I missing? Is there an easier way to do this? Can someone explain this to me like I'm two years old?
 
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  • #2
Do you really think L(x1+x2)=L(x1)+L(x2) holds for a)? I mean try x1=(0,0) and x2=(0,0).
 
  • #3
Dick said:
Do you really think L(x1+x2)=L(x1)+L(x2) holds for a)? I mean try x1=(0,0) and x2=(0,0).

I don't see it.
I thought x1 and x2 would be single values such as x1=0 and x2=0, not coordinates in two space such as (0,0). Is it the case if I'm going from R2->R2 and not R2->R3? I have very few examples in my class notes (my professor spends most of the class time on proofs, not examples) but both examples I do have has a single value for x1, x2, so on.If I try L(x1+x2)=L(x1)+L(x2) for x1=(0,0) and x2=(0,0) do I get (0,0,2)=(0,0,1)+(0,0,1)? The only thing I'm sure about is that I'm probably way off.
 
  • #4
It's just a difference in notation. Dick used x1 and x2 to represent vectors in R^2, where you're looking at x1 and x2 as coordinates of a single vector.

Here's what he said using different notation:
Do you really think L(u+v)=L(u)+L(v) holds for a)? I mean try u=(0,0) and v=(0,0).

Also, but more importantly, you are not understanding the definition of a linear transformation. You have this:
1. L(a*x1+b*x2)=aL(x1)+bL(x2);
2. L(x1+x2)=L(x1)+L(x2);
3. L(a*x1)=aL(x1);
In this definition x1 and x2 are vectors in R^2, not coordinates of a vector. It might be better for you to not to use indexes on vectors, but to use indexes on coordinates of vectors.

This makes your definition look like this:
1. L(a*u+b*v)=aL(u)+bL(v);
2. L(u+v)=L(u)+L(v);
3. L(a*u)=aL(u);

In the above, u = (u1, u2) and v = (v1, v2)

Got it?
 
  • #5
Mark44 said:
you are not understanding the definition of a linear transformation.
You sir, are correct.

I should have guessed that he was referring to vectors and not coordinates since essentially the problem is asking which of the four are valid transformations of vectors from two space to three space. Thank you for the explanation. However, I may have one more question on this depending on how the next step turns out.

So by rule 2, I have
L(u+v)=L(u)+L(v) => L(u1+v1, u2+v2)=>L(u1,u2)+L(v1,v2);
But if I go back to my original problem I'm not sure how the 1 in (u, v, 1)^t plays into this.


I'm trying my best not to sound like a blithering idiot.
 
  • #6
Daren H said:
If I try L(x1+x2)=L(x1)+L(x2) for x1=(0,0) and x2=(0,0) do I get (0,0,2)=(0,0,1)+(0,0,1)? The only thing I'm sure about is that I'm probably way off.

That is exactly right. L takes a 2-vector and gives you a 3-vector. So L((0,0))=(0,0,1). But (0,0)+(0,0)=(0,0). So L((0,0))=(0,0,1) is NOT equal to L((0,0))+L((0,0))=(0,0,2). (Sorry I'm skipping putting the transpose symbol (0,0)^T on.)
 
  • #7
Mark44, Dick; thanks for the help. Clearly I need more practice, so I might be back in this thread this weekend.
 

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 vector operations of addition and scalar multiplication. It is a fundamental concept in linear algebra and is often used to describe relationships between different sets of data.

2. How do you determine if a transformation is linear?

To determine if a transformation is linear, you can use the following two criteria:

  • The transformation must preserve vector addition. This means that for any two vectors u and v in the input space, the transformation of their sum T(u+v) must be equal to the sum of the individual transformations T(u) + T(v).
  • The transformation must preserve scalar multiplication. This means that for any scalar c and vector v in the input space, the transformation of the vector c*v must be equal to c times the transformation of v, T(c*v) = c*T(v).

3. What are examples of linear transformations?

Some common examples of linear transformations include rotations, reflections, and scaling. Other examples can include projections, shears, and translations. In general, any function that satisfies the two criteria for linearity mentioned above can be considered a linear transformation.

4. What is not a linear transformation?

A transformation that does not preserve either vector addition or scalar multiplication is not considered a linear transformation. This can include functions that involve non-linear operations such as taking the square root or raising to a power, or functions that involve non-linear variables such as trigonometric functions.

5. How is a linear transformation represented?

A linear transformation can be represented in several ways, including through matrices, systems of equations, and geometric transformations. In matrix form, a linear transformation from R2 to R3 can be represented as a 3x2 matrix, with each column representing the transformation of the standard basis vectors in R2. In systems of equations, a linear transformation can be represented as a set of equations that describe the output variables in terms of the input variables. Geometrically, a linear transformation can be represented as a mapping of points and vectors from one space to another.

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