Linear Transformations and matrix representation

If you mean the "a" values, it's because we're setting them to be 0 or 1 in order to find the images of the basis vectors. If you mean the "e" values, it's because we're substituting the basis vectors into the definition of T, and the other basis vectors have a coefficient of 0 in front of them.
  • #1
henry3369
194
0
Assume the mapping T: P2 -> P2 defined by:
T(a0 + a1t+a2t2) = 3a0 + (5a0 - 2a1)t + (4a1 + a2)t2
is linear.Find the matrix representation of T relative to the basis B = {1,t,t2}

My book says to first compute the images of the basis vector. This is the point where I'm stuck at because I'm not sure how the books arrives at the images:
T(b1) = T(1) = 3+5t
T(b2) = T(t) = -2t+4t2
T(b3) = T(t2) = t2

Where are these results coming from?
I don't understand where 1 is supposed to go to solve for T(1). I guess its the notation that is throwing me off. Usually when solving for a transformation, it has something such as T(x) = x^2, and you solve the transformation by substituting the value of the input for x. But now my input is 1 for an entire expression (a0 + a1t+a2t2)
 
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  • #2
henry3369 said:
Where are these results coming from?
Start with what you know. If you want T(1), look at your definition. In order to get T(1), put a0=1, a1=0 and a2=0. Then the definition says T(1)= 3⋅1 + (5⋅1 - 2⋅0)t + (4⋅0 + 0)t2. In the same way, to find T(t) , put a0=0, a1=1 and a2=0. The rest is left as an exercise...
 
  • #3
I'm not a fan of calling the functions 1, t and t2 (these are notations for numbers, not functions). I would define functions ##e_0, e_1, e_2## by
\begin{align*}
&e_0(x)=1\\
&e_1(x)=x\\
&e_2(x)=x^2
\end{align*} for all real numbers x. Then T is defined by ##T(a_0e_0+a_1e_1+a_2e_2)=3a_0e_0+(5a_0-2a_1)e_1+(4a_1+a_2)e_2## for all real numbers ##a_1,a_2,a_3##. Now let's do what Svein did, in my notation:
$$T(e_0)=T(1e_0+0e_1+0e_2)=3\cdot 1 e_0+(5\cdot 1-2\cdot 0)e_1+(4\cdot 0+0)e_2=3e_0+5e_1.$$
 
  • #4
Svein said:
Start with what you know. If you want T(1), look at your definition. In order to get T(1), put a0=1, a1=0 and a2=0. Then the definition says T(1)= 3⋅1 + (5⋅1 - 2⋅0)t + (4⋅0 + 0)t2. In the same way, to find T(t) , put a0=0, a1=1 and a2=0. The rest is left as an exercise...
How do you know which ax corresponds to the input? For T(1), you set a0 =1 and for T(t) you set a1 = 1. I don't think it could be the order in which it appears in B, because B is just a set of vectors and the order shouldn't matter.
 
  • #5
Fredrik said:
I'm not a fan of calling the functions 1, t and t2 (these are notations for numbers, not functions). I would define functions ##e_0, e_1, e_2## by
\begin{align*}
&e_0(x)=1\\
&e_1(x)=x\\
&e_2(x)=x^2
\end{align*} for all real numbers x. Then T is defined by ##T(a_0e_0+a_1e_1+a_2e_2)=3a_0e_0+(5a_0-2a_1)e_1+(4a_1+a_2)e_2## for all real numbers ##a_1,a_2,a_3##. Now let's do what Svein did, in my notation:
$$T(e_0)=T(1e_0+0e_1+0e_2)=3\cdot 1 e_0+(5\cdot 1-2\cdot 0)e_1+(4\cdot 0+0)e_2=3e_0+5e_1.$$
So what makes
e0(x)=1 and not e0(x)=x? If the order of the vectors in the Basis changed, how would I know that e0(x)=1? . Also, why are the others always zero?
 
  • #6
henry3369 said:
How do you know which ax corresponds to the input? For T(1), you set a0 =1 and for T(t) you set a1 = 1. I don't think it could be the order in which it appears in B, because B is just a set of vectors and the order shouldn't matter.
Yes, strictly speaking, it's ambiguous to talk about the components of a vector in a specific basis. We should always be talking about the components of a vector with respect to an ordered basis like the triple ##(b_1,b_2,b_3)## rather than the components of a vector with respect to the basis ##\{b_1,b_2,b_3\}##. Unfortunately people are sloppy with the language. But they're at least being sloppy in a consistent way. When they talk about the components of a vector with respect to ##\{b_1,b_2,b_3\}##, they always mean with respect to ##(b_1,b_2,b_3)##, and never with respect to e.g. ##(b_3,b_1,b_2)##.

henry3369 said:
So what makes
e0(x)=1 and not e0(x)=x? If the order of the vectors in the Basis changed, how would I know that e0(x)=1?
The way I did it is just a convention. You could number the functions differently if you want to.

Now you're probably thinking "wait a minute, the formula for the number on row i, column j of the matrix depends on the order of the basis vectors, so each choice of how to order them could give me a different matrix". This would be a correct observation. A linear operator and a basis don't uniquely determine a matrix. A linear operator and an ordered basis on the other hand...

In this problem, it's safe to assume that you should find the matrix of T with respect to the ordered basis ##(b_1,b_2,b_3)## (i.e. my ##(e_0,e_1,e_2)##).

henry3369 said:
Also, why are the others always zero?
I'm not sure what others you're referring to.
 
Last edited:

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another, while preserving the basic structure of the original space. This means that the transformation preserves the operations of vector addition and scalar multiplication.

2. How is a linear transformation represented by a matrix?

A linear transformation can be represented by a matrix in a process called matrix representation. The columns of the matrix are the images of the basis vectors of the original space, and the transformation of any vector can be found by multiplying the vector by the matrix.

3. What is the difference between a linear transformation and a matrix transformation?

A linear transformation is a general concept that can be represented by various mathematical objects, including matrices. A matrix transformation, on the other hand, specifically refers to a transformation that can be represented by a matrix.

4. How can you determine if a transformation is linear?

A transformation is linear if it satisfies two properties: preservation of addition and preservation of scalar multiplication. This means that the transformation of the sum of two vectors is equal to the sum of their individual transformations, and the transformation of a vector multiplied by a scalar is equal to the scalar multiplied by the transformation of the original vector.

5. What is the importance of linear transformations?

Linear transformations are fundamental in many areas of mathematics and science, including geometry, physics, and computer science. They allow us to simplify and solve complex problems, and they have numerous applications in fields such as image processing, data compression, and computer graphics.

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