Another linear transformation question

In summary, the conversation discusses how to determine if a given transformation T is linear. The main criteria for linearity is that T(kx) = k * T(x) for any scalar k and vector x. The conversation provides an example where this condition is not satisfied, meaning that T is not linear. This counter-example is sufficient to prove that T is not linear, as it only takes one instance to disprove linearity. Additionally, it is important to verify that both properties of linearity (T(u+v) = T(u) + T(v) and T(cu) = cT(u)) are satisfied for all possible vectors.
  • #1
KataKoniK
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Q: In each case, show that T is not a linear transformation.

T[x y]^T = [0 y^2]^T

A: If X = [0 1]^T then T(2X) = [0 4]^T while 2T(X) = [0 2]^T



I don't quite understand this solution. What are we trying to accomplish here? So, since T(2X) = [0 4]^T while 2T(X) = [0 2]^T do not yeild the same answer, then it's not linear? If the answer was T(2X) = [0 4]^T while 2T(X) = [0 4]^T, then it would be linear?
 
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  • #2
One of the defining characteristics of a linear transformation f (with some given domain/codomain) is that f(kx) = k * f(x) (k is any scalar, x any vector). So if we have a function (like your "T") which /doesn't/ satisfy the previous condition, it can't be linear.

However, just because a function satisfies f(kx) = k * f(x) for some pair k, x doesn't mean it's linear. It may fail to satisfy f(kx) = k * f(x) for some other pair of k, x, or it might not satisfy f(x + y) = f(x) + f(y) (x, y vectors).
 
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  • #3
To check if a transformation is linear, you must verify that the two following properties are satisfied:

1. T(u+v)=T(u)+T(v)

2.T(cu)=cT(u)

That is the definition.

What the author does is that he gives a counter-exemple of 2. I.e. he gives a specific case (u = (0,1)) where 2. fails. And this is sufficient to conclude that T is not linear.

Now I hope you understand that simply verifying that "T(2X) = [0 4]^T while 2T(X) = [0 4]^T" wouldn't prove anything. To conclude that T is linear, you'd have to show that properties 1. AND 2. are satisfied for EVERY imaginable vector.
 
  • #4
Understood. Thanks a lot
 

FAQ: Another linear transformation question

What is a linear transformation?

A linear transformation is a mathematical operation that maps one vector space to another while preserving the basic structure of the space. In simpler terms, it is a function that takes in a vector as an input and produces another vector as an output.

How is a linear transformation different from other types of transformations?

A linear transformation is different from other types of transformations because it follows the properties of linearity, which include preserving vector addition and scalar multiplication. This means that the output of a linear transformation is always a combination of the input vectors, and the transformation does not change the direction or magnitude of the vectors.

What are some examples of linear transformations?

Some examples of linear transformations include rotations, reflections, and scaling. In these cases, the transformation is still considered linear as long as the properties of linearity are preserved. For example, a reflection still preserves vector addition and scalar multiplication.

How are linear transformations represented mathematically?

Linear transformations can be represented using matrices or transformation equations. Matrices are used to represent the transformation in terms of its effect on the standard basis vectors, while transformation equations show the input and output vectors in terms of their components.

Why are linear transformations important in science?

Linear transformations are important in science because they can be used to model real-world phenomena and solve complex problems. They are also used in many fields such as physics, engineering, and computer science. Additionally, linear transformations have many applications in data analysis and machine learning.

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