Linear Transformations: Solving (iii)

In summary, the conversation discusses a homework problem involving finding the matrix representation of a linear transformation in an ordered basis, determining if a set of functions form a basis, and using coordinate vectors to solve a problem. It also includes a discussion on the correct approach and a potential mistake in the solution.
  • #1
Ted123
446
0

Homework Statement



[PLAIN]http://img219.imageshack.us/img219/2950/linl.jpg

Homework Equations



The Attempt at a Solution



Is this how I do part (iii)?

From (ii) I get:

[itex]M^{\mathcal C}_{\mathcal C} (\phi) = \begin{bmatrix} 1 & 3 & 2 \\ 1 & -3 & 0 \\ 0 & 0 & 2 \end{bmatrix}[/itex]

3(iii)

[itex]M^{\mathcal C}_{\mathcal C} (\phi) {\bf v} = {\bf w}[/itex] where [itex]{\bf w}[/itex] is the coordinate vector of [itex]\phi (v)[/itex] with respect to [itex]\mathcal C[/itex] and [itex]{\bf v}[/itex] is the coordinate vector of [itex]v\in V[/itex] .

[itex]{\bf v} = \begin{bmatrix} 1 \\ -3 \\ \pi \end{bmatrix}[/itex]

So [itex]{\bf w} = \begin{bmatrix} 1 & 3 & 2 \\ 1 & -3 & 0 \\ 0 & 0 & 2 \end{bmatrix} \begin{bmatrix} 1 \\ -3 \\ \pi \end{bmatrix} = \begin{bmatrix} 2\pi -8 \\ 10 \\ 2\pi \end{bmatrix}[/itex]
 
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  • #2
Yes, that's the correct method.
 
  • #3
Is the matrix M^C_C(phi) supposed to be the matrix of phi in the ordered basis C? If so, your matrix is incorrect; 1+x, 1-x, and 1+x^2 are eigenvectors of phi, with eigenvalues 1, 3, and 2, respectively. The matrix you are looking for is a diagonal matrix.

Your approach for (iii) is correct; simply multiply the matrix of phi in the basis C by the coordinate matrix v; that will give you the coordinate matrix of phi(v).
 
  • #4
I concur. I get a different matrix for part (ii).
 
  • #5
ns2675 said:
Is the matrix M^C_C(phi) supposed to be the matrix of phi in the ordered basis C? If so, your matrix is incorrect; 1+x, 1-x, and 1+x^2 are eigenvectors of phi, with eigenvalues 1, 3, and 2, respectively. The matrix you are looking for is a diagonal matrix.

Your approach for (iii) is correct; simply multiply the matrix of phi in the basis C by the coordinate matrix v; that will give you the coordinate matrix of phi(v).

Well this is my approach to the whole question:

Part (i)

[itex]\phi (1) = 2-x = 2(1) -1(x) + 0(x^2)[/itex]

[itex]\phi (x) = -1 + 2x = -1(1) + 2(x) + 0(x^2)[/itex]

[itex]\phi (x^2) = x + 2x^2 = 0(1) + 1(x) + 2(x^2)[/itex]

[itex]M^{\mathcal B}_{\mathcal B}(\phi) = \begin{bmatrix} 2 & -1 & 0 \\ -1 & 2 & 1 \\ 0 & 0 & 2 \\ \end{bmatrix}[/itex]

Part (ii)

Since [itex]{\mathcal C}[/itex] has size 3 and [itex]\text{dim}(\mathbb{R}_2[x])=3[/itex] it is enough to show that [itex]\mathcal C}[/itex] is either linearly independent or spans [itex]V[/itex] . For completeness let's do both.

[itex]{\mathcal C}[/itex] is linearly independent if

[itex]\alpha (1+x) + \beta (1-x) + \gamma (1+x^2) = 0 \Rightarrow \alpha = \beta = \gamma = 0[/itex]

[itex]\alpha + \alpha x +\beta - \beta x + \gamma + \gamma x^2 = 0[/itex]

[itex]\alpha + \beta + \gamma + (\alpha - \beta)x + \gamma x^2 = 0[/itex]

Equate coefficients.

[itex]\alpha + \beta + \gamma = 0[/itex] (1)

[itex]\alpha - \beta = 0 \Rightarrow \alpha = \beta[/itex] (2)

[itex]\gamma = 0[/itex] (3)

Subbing (2) and (3) in (1) gives [itex]2\alpha = 0 \Rightarrow \alpha = 0 \Rightarrow \beta = 0[/itex] from (2).

Hence [itex]\alpha = \beta = \gamma = 0[/itex] and [itex]\mathcal C[/itex] is linearly independent.

[itex]\mathcal C[/itex] spans [itex]V[/itex] if

[itex]a_1 (1+x) + a_2 (1-x) + a_3 (1+x^2) = b_1 + b_2 x + b_3 x^2[/itex]

has at least one solution for every set of coefficients [itex]b_1,b_2,b_3[/itex]

[itex]a_1 + a_2 + a_3 + (a_1 - a_2)x + a_3x^2 = b_1 + b_2 x + b_3 x^2[/itex]

Equating coefficients yields the following system of equations (*):

[itex]a_1 + a_2 + a_3 = b_1[/itex]

[itex]a_1 - a_2 = b_2[/itex]

[itex]a_3 = b_3[/itex]

Transforming the augmented matrix

[itex]\begin{bmatrix} 1 & 1 & 1 & 1 & 0 & 0 \\ 1 & -1 & 0 & 0 & 1 & 0 \\ 0 & 0 & 1 & 0 & 0 & 1 \\ \end{bmatrix}[/itex]

into row echelon form we get

[itex]\begin{bmatrix} 1 & 0 & 0 & \frac{1}{2} & \frac{1}{2} & -\frac{1}{2} \\ 0 & 1 & 0 & \frac{1}{2} & -\frac{1}{2} & -\frac{1}{2} \\ 0 & 0 & 1 & 0 & 0 & 1 \\ \end{bmatrix}[/itex]

which corresponds to the following system of equations:

[itex]a_1 = \frac{1}{2}b_1 + \frac{1}{2}b_2 -\frac{1}{2}b_3[/itex]

[itex]a_2 = \frac{1}{2}b_1 -\frac{1}{2}b_2 -\frac{1}{2}b_3[/itex]

[itex]a_3 = b_3[/itex]

This system {and therefore the system (*)} are consistent for all RHS values, therefore [itex]\mathcal C[/itex] spans [itex]V[/itex] .

We can therefore conclude that [itex]\mathcal C[/itex] is certainly a basis for [itex]V[/itex] .

[itex]\phi (1+x) = (2-1) + (-1+2)x = 1 + x = 1(1) + 1(x) + 0(x^2)[/itex]

[itex]\phi (1-x) = (2+1) + (-1-2)x = 3-3x = 3(1) - 3(x) + 0(x^2)[/itex]

[itex]\phi (1+x^2) = 2 + (-1+1)x + 2x^2 = 2+2x^2 = 2(1) + 0(x) + 2(x^2)[/itex]

[itex]M^{\mathcal C}_{\mathcal C} (\phi) = \begin{bmatrix} 1 & 3 & 2 \\ 1 & -3 & 0 \\ 0 & 0 & 2 \end{bmatrix}[/itex]

Part (iii)

[itex]M^{\mathcal C}_{\mathcal C} (\phi) {\bf v} = {\bf w}[/itex] where [itex]{\bf w}[/itex] is the coordinate vector of [itex]\phi (v)[/itex] with respect to [itex]\mathcal C[/itex] and [itex]{\bf v}[/itex] is the coordinate vector of [itex]v\in V[/itex] .

[itex]{\bf v} = \begin{bmatrix} 1 \\ -3 \\ \pi \end{bmatrix}[/itex]

So [itex]{\bf w} = \begin{bmatrix} 1 & 3 & 2 \\ 1 & -3 & 0 \\ 0 & 0 & 2 \end{bmatrix} \begin{bmatrix} 1 \\ -3 \\ \pi \end{bmatrix} = \begin{bmatrix} 2\pi -8 \\ 10 \\ 2\pi \end{bmatrix}[/itex]
 
  • #6
You don't need to do all of that work. For (i) (and (ii)) simply find the coordinate matrices of phi(1), phi(x) and phi(x^2) in the ordered basis B (or phi(1+x), phi(1-x), phi(1+x^2) in the ordered basis C). These coordinate matrices are, in order, the columns of the matrix you seek.

Now, for part (ii), to show that those three functions are a basis, it suffices to show that they are linearly independent. This is not difficult to do, and your way works fine.

If M^B_B(phi) is indeed meant to represent the matrix of phi in the ordered basis B, then I think your only mistake is that you are misunderstanding what this matrix is.
 
  • #7
ns2675 said:
You don't need to do all of that work. For (i) (and (ii)) simply find the coordinate matrices of phi(1), phi(x) and phi(x^2) in the ordered basis B (or phi(1+x), phi(1-x), phi(1+x^2) in the ordered basis C). These coordinate matrices are, in order, the columns of the matrix you seek.

Now, for part (ii), to show that those three functions are a basis, it suffices to show that they are linearly independent. This is not difficult to do, and your way works fine.

If M^B_B(phi) is indeed meant to represent the matrix of phi in the ordered basis B, then I think your only mistake is that you are misunderstanding what this matrix is.

The matrix for (1) is definitely correct (I was told it was by the lecturer) so the method I am using is correct (and is what we were taught).

I know I only need to show EITHER linear independence OR span for part (ii) but for COMPLETENESS (and to make sure I could do both) I did BOTH!

[PLAIN]http://img832.imageshack.us/img832/1083/matrixtp.jpg

I've just noticed that I've wrote the images of the vectors in C as a linear combination of the vectors in B rather than C for part (ii)...
 
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  • #8
Sorry, just realized you already wrote that; I was confusing the numbers on your question. Under part (i) I think you wrote something different on the solution from what was written in the problem; it seems as though part (i) of the problem is under part (ii) of your solution. Yes, you are exactly right.
 
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  • #9
Just read your last post; that definition is what I was thinking of.

I didn't mean to reiterate that point about only needing to show independence; I see you already wrote that.

Hope I could help.
 
  • #10
ns2675 said:
The coordinate matrices for phi(1), phi(x), phi(x^2) in the ordered basis B be should be obtained as follows.

phi(1) = 2 - x = 2(1) - 1(x) + 0(x^2);
phi(x) = -1 + 2x = -1(1) + 2(x) + 0(x^2);
phi(x^2) = x + 2x^2 = 0(1) + 1(x) + 2(x^2).

(Wish I knew how to use Tex, for your sake!) From the above equations, you can read of the coordinates; those are the columns of your matrix. In fact, your matrix should be the transpose of the coefficient matrix of the above system of equations. Deal similarly with the ordered basis C (it's a bit easier; as I said before, the elements of C are each eigenvectors of phi).

I know - that's how I got the matrix for part (i)!

Sorry I just noticed I posted my working to a completely different question before...
 
  • #11
Ted123 said:
I've just noticed that I've wrote the images of the vectors in C as a linear combination of the vectors in B rather than C for part (ii)...
Yeah, that's your mistake.
 
  • #12
Sorry about that confusion, I just realized that.
 
  • #13
ns2675 said:
Just read your last post; that definition is what I was thinking of.

I didn't mean to reiterate that point about only needing to show independence; I see you already wrote that.

Hope I could help.

Sorry everyone!

Just to clarify I posted my working to a completely different question before and mixed up the numbering of the question parts in my working!

It seems my mistake for part (ii) is writing the images as a linear combination of vectors in B rather than C.
 
  • #14
vela said:
Yeah, that's your mistake.

So is this correct:

[itex]M^{\mathcal C}_{\mathcal C} (\phi) = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 3 & 0 \\ 0 & 0 & 2 \end{bmatrix}[/itex]

(And obviously then I've got to correct part (iii) )
 
  • #15
Yeah, that's the right matrix.
 

Related to Linear Transformations: Solving (iii)

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 space's algebraic structure. In simpler terms, it is a transformation that preserves lines and the origin.

2. How do you solve a linear transformation?

To solve a linear transformation, you first need to define the transformation matrix, which represents the transformation in terms of its coefficients. Then, you can apply the transformation to any vector by multiplying it with the transformation matrix. The resulting vector will be the transformed vector.

3. What is the purpose of solving linear transformations?

The purpose of solving linear transformations is to understand how a transformation affects a vector or a set of vectors. It allows us to visualize and manipulate data in different ways, making it easier to analyze and interpret the data.

4. Can you give an example of a linear transformation?

One example of a linear transformation is a scaling transformation, where the size of a vector is changed without changing its direction. For instance, if we multiply a vector by a factor of 2, the resulting vector will be twice as long as the original vector.

5. What are the properties of linear transformations?

Some of the properties of linear transformations include preserving linearity, preserving the origin, and preserving the magnitude and direction of vectors. Additionally, linear transformations can be composed and inverted, and the composition of two linear transformations is also a linear transformation.

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