Linear transformation (minor clarification)

In summary, the conversation discusses the conditions for a function, f, to be linear. The first condition is if y =/= 0, then f(x,y) = x^2/y. The second condition is if y = 0, then f(x,y) = 0. The conversation involves testing these conditions by using specific values for u and v and evaluating their sums and the sum evaluated at u+v. Ultimately, it is determined that the function is not linear.
  • #1
negation
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Homework Statement



Capture.PNG



The Attempt at a Solution



I don't think I'm interpreting the question correctly. Maybe someone can point me in the right direction?

There are 2 conditions: if y =/=0 then f(x,y) = x^2/y and if y=0 then f(x,y) = 0

Let u =(1,1) and v = (1,1)

f(v) = f(1,1) = 1^2/1 = 1

f(u) = f(1,1) = 1

f(u) + f(v) = 2

f(u+v) = 2

testing for the second condition: if y=0 then f(x,y) = 0

let u = (1,0) v = (1,0)

f(u) = 0 since if y = 0, f(x,y) = 0

f(v) = 0

f(u) + f(v) = 0

f(u+v) = f(2,0) = 0
 
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  • #2
negation said:

Homework Statement



View attachment 68313

The Attempt at a Solution



I don't think I'm interpreting the question correctly. Maybe someone can point me in the right direction?
I assume that the goal is to check whether ##f## is linear. So your approach is fine:
Let u =(1,1) and v = (1,1)

f(v) = f(1,1) = 1^2/1 = 1

f(u) = f(1,1) = 1

f(u) + f(v) = 2
OK so far.
f(u+v) = 2
No, ##u+v = (1,1) + (1,1) = (2,2)##. What do you get when you evaluate ##f## at this point?

[edit] Oops, I miscalculated. Yes, you get 2. So this shows that ##f(u+v) = f(u) + f(v)## for that particular choice of ##u## and ##v##. But to conclude linearity, you need to show that it is true for all choices of ##u## and ##v##.

Hint: try another choice of ##u## and ##v##.
 
  • #3
jbunniii said:
I assume that the goal is to check whether ##f## is linear. So your approach is fine:

OK so far.

No, ##u+v = (1,1) + (1,1) = (2,2)##. What do you get when you evaluate ##f## at this point?

[edit] Oops, I miscalculated. Yes, you get 2. So this shows that ##f(u+v) = f(u) + f(v)## for that particular choice of ##u## and ##v##. But to conclude linearity, you need to show that it is true for all choices of ##u## and ##v##.

Hint: try another choice of ##u## and ##v##.

I presume we are referring to the first condition in the question where x^2/y ?

I tried a few values but they all preserve addition.
 
  • #4
negation said:
I presume we are referring to the first condition in the question where x^2/y ?
Yes, the ##y=0## case is unlikely to provide a counterexample for additivity.

I tried a few values but they all preserve addition.
You should be able to find a simple counterexample using points of the form ##(a,1)## and ##(b,1)##
 
  • #5
That "[itex]x^2[/itex]" makes me suspicious! So I would try u= (2, 1) and v= (3, 1).

What are f(u) and f(v)? What is f(u)+ f(v)?

u+ v= (5, 2). What is f(u+ v)?
 
  • #6
HallsofIvy said:
That "[itex]x^2[/itex]" makes me suspicious! So I would try u= (2, 1) and v= (3, 1).

What are f(u) and f(v)? What is f(u)+ f(v)?

u+ v= (5, 2). What is f(u+ v)?

Halls!

Any reason as to why you chose those values for u and v? Are they just arbitrary?

f(u) = f(2,1) = 4 f(v) = f(3,1) = 9
f(u) + f(v) = 13

u + v = (5,1)

f(u+v) = 25
 
  • #7
negation said:
Any reason as to why you chose those values for u and v? Are they just arbitrary?
Almost any choice of u and v will work, but the calculation of ##\frac{(u_1)^2}{u_2}+\frac{(v_1)^2}{v_2}## is easier when ##u_2=v_2##, and is especially easy when ##u_2=v_2=1##.
 
  • #8
negation said:
Halls!

Any reason as to why you chose those values for u and v? Are they just arbitrary?

f(u) = f(2,1) = 4 f(v) = f(3,1) = 9
f(u) + f(v) = 13

u + v = (5,1)
You added ##(2,1)## to ##(3,1)## and got ##(5,1)##?
 

FAQ: Linear transformation (minor clarification)

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 basic structure of the original space. It is a fundamental concept in linear algebra and is used to describe many real-world phenomena in fields such as physics, economics, and engineering.

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

A linear transformation is unique in that it follows two important properties: additivity and homogeneity. Additivity means that the transformation of the sum of two vectors is equal to the sum of the individual transformations, while homogeneity states that the transformation of a scalar multiple of a vector is equal to the scalar multiple of the transformation of the original vector. This makes linear transformations useful for solving systems of linear equations and studying the behavior of linear systems.

What is the standard form of a linear transformation?

The standard form of a linear transformation is a matrix notation, where a transformation is represented by a matrix that contains the coefficients for the variables in the transformation. For example, a transformation in two dimensions can be represented by a 2x2 matrix, while a transformation in three dimensions would require a 3x3 matrix.

How do you determine if a transformation is linear?

To determine if a transformation is linear, you can apply the additivity and homogeneity properties. If the transformation satisfies both of these properties, then it is considered linear. Another way to determine linearity is by checking if the transformation preserves the shape and size of the original vectors. If the transformation stretches, rotates, or reflects the vectors, then it is not linear.

What is the importance of linear transformations in data analysis?

Linear transformations are essential in data analysis because they can be used to transform data in a way that simplifies calculations and reveals patterns or relationships. They are commonly used in machine learning algorithms, data preprocessing, and feature engineering. Additionally, linear transformations are useful for dimension reduction, which can help make complex data more manageable and easier to interpret.

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