You're welcome. Glad I could help.

The second column of the matrix represents the transformation of (0, 1), which is the second basis vector. By finding linear combinations of these two vectors, we are essentially finding the transformation of any arbitrary vector in R2. This is why finding a new basis is necessary and why we are trying to find linear combinations that equal (1, 0) and (0, 1).To answer your question about finding a transformation that maps (2, 3) and (3, 5) to (1, 0), this is possible but it would not represent the same linear transformation as the one given in the problem. In
  • #1
auk411
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0

Homework Statement



Will the values given below define a unique linear transformation? If so, find the value of T for an arb. domain element.

T(3,5) = (1,2) and T(2,3) = (6,7)

Homework Equations


The Attempt at a Solution

T(a[3,5]) + T(b[2,3]) = aT[3,5] + bT[2,3]
= a(1,2) + b(6,7)
=(a +6b, 2a +7)

This is wrong. But I have no idea how to even begin answering it. After reading the textbook, reading my notes, listening to the lecture audio, spending hours online reading, spending more hours just playing around with the problem, I am officially more lost and confused on the subject of linear transformations. I know what the answer is, btw. I just have no idea how to get there.
 
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  • #2
auk411 said:

Homework Statement



Will the values given below define a unique linear transformation? If so, find the value of T for an arb. domain element.

T(3,5) = (1,2) and T(2,3) = (6,7)

Homework Equations





The Attempt at a Solution




T(a[3,5]) + T(b[2,3]) = aT[3,5] + bT[2,3]
= a(1,2) + b(6,7)
=(a +6b, 2a +7)

This is wrong. But I have no idea how to even begin answering it. After reading the textbook, reading my notes, listening to the lecture audio, spending hours online reading, spending more hours just playing around with the problem, I am officially more lost and confused on the subject of linear transformations. I know what the answer is, btw. I just have no idea how to get there.
Here's a start.
1. Write (1, 0) as a linear combination of (3, 5) and (2, 3).
IOW, find constants a and b so that (1, 0) = a(3, 5) and + b(2, 3)
This entails solving the system
3a + 2b = 1
5a + 3b = 0

2. Write (0, 1) as a linear combination of (3, 5) and (2, 3).
IOW, find constants c and d so that (0,1) = c(3, 5) and + d(2, 3)
Similar to step 1.
 
  • #3
Mark44 said:
Here's a start.
1. Write (1, 0) as a linear combination of (3, 5) and (2, 3).
IOW, find constants a and b so that (1, 0) = a(3, 5) and + b(2, 3)
This entails solving the system
3a + 2b = 1
5a + 3b = 0

2. Write (0, 1) as a linear combination of (3, 5) and (2, 3).
IOW, find constants c and d so that (0,1) = c(3, 5) and + d(2, 3)
Similar to step 1.

Thank you.

However, this really confuses me. WHY are we doing this? I have no examples in my book or by the teacher in class that do anything like this. So it is never mentioned that we should go about solving for (1,0) and (0,1). So even though I know your way leads to the right answer (I've worked out the problem your way) I have no idea WHY I am doing what I am doing.
 
  • #4
Because as I said and HallsOfIvy said in the other thread you started, if you know what a linear transformation does to its basis vectors, you know everything there is to know about the transformation. In particular, with this information, you can write a matrix that represents the linear transformation.
 
  • #5
We already had a basis. So why do we need to find a new basis? Also, why are we trying to find a linear COMBINATION that equals, say, (1,0).

Couldn't we try to find a transition that takes in (2,3) and (3,5) and gives us (1,0)?
 
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  • #6
auk411 said:
We already had a basis. So why do we need to find a new basis? Also, why are we trying to find a linear COMBINATION that equals, say, (1,0).
Because some bases are easier to work with than others. In R2, the standard basis is <1, 0>, <0, 1>. It's simple to decompose any vector in R2 into a linear combination of these two vectors. For example, the vector <3, 2> can be written as 3<1, 0> + 2<0, 1>, and this can be obtained by inspection. How easy is it to write <3, 2> as a linear combination of <2, 3> and <3, 5>?
auk411 said:
Couldn't we try to find a transition that takes in (2,3) and (3,5) and gives us (1,0)?
Essentially that's what we're doing, but that's a different transformation than the one you're trying to find.
 
  • #7
auk411 said:
We already had a basis. So why do we need to find a new basis? Also, why are we trying to find a linear COMBINATION that equals, say, (1,0).

Couldn't we try to find a transition that takes in (2,3) and (3,5) and gives us (1,0)?
Of course, we could! But that is not the problem you cited before. Also, note that since you have two different vectors being mapped to the same one, the linear transformation is not "one-to-one" and so is not invertible. In fact, it will map all of R2 to the one dimensional subspace spanned by (1, 0): (x, 0) for x any number.

(1, 0)= a(2, 3)+ b(3, 5) gives 2a+ 3b= 1, 3a+ 5b= 0. Subtract twice the second equation from three times the first to eliminate a: -b= 3 so b= -3. Then 2a- 9= 1, a= 5. That is,
(1, 0)= 5(2,3)- 3(9,5) so that T(1, 0)= 5T(2, 3)- 3T(9, 5)= 5(1, 0)- 3(1, 0)= (2, 0). The first column of the matrix representing this linear transformation in the standard basis.

(0, 1)= a(2, 3)+ b(3, 9) gives 2a+ 3b= 0, 3a+ 5b= 1. Again, subtract twice the second equation from three times the first to eliminate a: -b= -2 so b= 2. Then 2a+ 6= 0 so a= -3.
(0, 1)= -3(2, 3)+ 2(3, 9) so that T(0, 1)= -3T(2, 3)+ 2T(3, 9)= -3(1, 0)+ 2(1, 0)= (-1, 0).

The matrix representing this linear transformation, in the standard basis, is
[tex]\begin{bmatrix}2 & -1 \\ 0 & 0\end{bmatrix}[/tex]
Obviously that has 0 determinant and is not invertible. It is easy to see that the null space is given by (x, 2x) and the and the range by (x, 0) which is, as said before, spanned by (1, 0).
 
  • #8
Mark44 said:
Because some bases are easier to work with than others. In R2, the standard basis is <1, 0>, <0, 1>. It's simple to decompose any vector in R2 into a linear combination of these two vectors. For example, the vector <3, 2> can be written as 3<1, 0> + 2<0, 1>, and this can be obtained by inspection. How easy is it to write <3, 2> as a linear combination of <2, 3> and <3, 5>?
Essentially that's what we're doing, but that's a different transformation than the one you're trying to find.



Thanks a billion times over!
 
  • #9
HallsofIvy said:
Of course, we could! But that is not the problem you cited before. Also, note that since you have two different vectors being mapped to the same one, the linear transformation is not "one-to-one" and so is not invertible. In fact, it will map all of R2 to the one dimensional subspace spanned by (1, 0): (x, 0) for x any number.

(1, 0)= a(2, 3)+ b(3, 5) gives 2a+ 3b= 1, 3a+ 5b= 0. Subtract twice the second equation from three times the first to eliminate a: -b= 3 so b= -3. Then 2a- 9= 1, a= 5. That is,
(1, 0)= 5(2,3)- 3(9,5) so that T(1, 0)= 5T(2, 3)- 3T(9, 5)= 5(1, 0)- 3(1, 0)= (2, 0). The first column of the matrix representing this linear transformation in the standard basis.

(0, 1)= a(2, 3)+ b(3, 9) gives 2a+ 3b= 0, 3a+ 5b= 1. Again, subtract twice the second equation from three times the first to eliminate a: -b= -2 so b= 2. Then 2a+ 6= 0 so a= -3.
(0, 1)= -3(2, 3)+ 2(3, 9) so that T(0, 1)= -3T(2, 3)+ 2T(3, 9)= -3(1, 0)+ 2(1, 0)= (-1, 0).

The matrix representing this linear transformation, in the standard basis, is
[tex]\begin{bmatrix}2 & -1 \\ 0 & 0\end{bmatrix}[/tex]
Obviously that has 0 determinant and is not invertible. It is easy to see that the null space is given by (x, 2x) and the and the range by (x, 0) which is, as said before, spanned by (1, 0).

Thank you for many replies. I think you took my question the wrong way. But I still got something from your post.

thanks!
 

FAQ: You're welcome. Glad I could help.

What is a unique linear transformation?

A unique linear transformation is a mathematical function or mapping from one vector space to another that preserves the linear structure of the vectors. This means that the transformation only involves operations such as addition, subtraction, and scalar multiplication.

How is a unique linear transformation different from a general linear transformation?

A unique linear transformation is a specific type of linear transformation that has the added property of being one-to-one and onto, also known as a bijective transformation. This means that each element in the original vector space has a unique mapping to an element in the transformed vector space, and vice versa.

Can a unique linear transformation be represented as a matrix?

Yes, a unique linear transformation can be represented as a matrix. This is because a unique linear transformation is a linear function, and linear functions can be represented by matrices using a standard basis.

What are some applications of unique linear transformations?

Unique linear transformations have many applications in mathematics, engineering, and computer science. They are commonly used in computer graphics to transform and manipulate images, in cryptography to encrypt and decrypt messages, and in physics to describe the transformation of physical quantities between coordinate systems.

How can I determine if a transformation is unique and linear?

To determine if a transformation is unique and linear, you can check if it satisfies the properties of linearity and uniqueness. Linearity means that the transformation preserves vector addition and scalar multiplication, while uniqueness means that each element in the original vector space has a unique mapping to an element in the transformed vector space. You can also check if the transformation can be represented as a matrix and if it is one-to-one and onto.

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