LINEAR ALGEBRA: image of vectors through other basis

In summary, the homework statement is to find the orthonormal basis made of the following vectors and the endomorphism such that M^{B,B}_{\phi}=A.
  • #1
Felafel
171
0

Homework Statement



In ##E^3##, given the orthonormal basis B, made of the following vectors ## v_1=\frac{1}{\sqrt{2}}(1,1,0); v_2=\frac{1}{\sqrt{2}}(1,-1,0); v_3=(0,0,1)##
and the endomorphism ##\phi : E^3 \to E^3## such that ##M^{B,B}_{\phi}##=A where
(1 0 0)
(0 2 0) = A
(0 0 0)
find:
##\phi(e_1), \phi(e_2), \phi(e_3)## written with respect to the canonical basis
(where e1, e2, e3 are vector of the canonical basis ##\mathcal{E}##)


The Attempt at a Solution



here is what I thought, but having no solutions i don't know if it is correct:

I write the vectors of the canonical basis as combination of the vectors of B, also obtaining the ##M^{B,\mathcal{E}}##:

(1,0,0)=##a_{11} \cdot \frac{1}{\sqrt{2}}(1,1,0)+ a_{21} \cdot \frac{1}{\sqrt{2}}(1,-1,0)+ a_{31} \cdot (0,0,1)##

(0,1,0)=##a_{12} \cdot \frac{1}{\sqrt{2}}(1,1,0)+ a_{22} \cdot \frac{1}{\sqrt{2}}(1,-1,0)+ a_{32} \cdot (0,0,1)##

(0,0,1)=##a_{13} \cdot \frac{1}{\sqrt{2}}(1,1,0)+ a_{23} \cdot \frac{1}{\sqrt{2}}(1,-1,0)+ a_{33} \cdot (0,0,1)##

getting:

(##\frac{\sqrt{2}}{2}## ##\frac{\sqrt{2}}{2}## 0)
(##\frac{\sqrt{2}}{2}## -##\frac{\sqrt{2}}{2}## 0)= ##M^{B, \mathcal{E}}##
( 0 0 1)

so ##\phi(e_1), \phi(e_2), \phi(e_3)## are the columns of this matrix.
Is it correct?
thank you in advance :)
 
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  • #2
I haven't thought it all through, but it doesn't look correct to me. I don't see why those columns would contain the ##\mathcal E##-components of the ##\phi(e_i)##. Do you have an argument for it? Also, I think I have calculated ##\phi(e_3)##, and my result is different from yours.

I think the strategy here should be to start with
$$\phi(e_i)=\phi\left( \sum_j M^{B,\mathcal E}_{ji} v_j\right) =\sum_j M^{B,\mathcal E}_{ji}\phi(v_i).$$ Now you just have to compute the ##\phi(v_i)##. (Did I understand your definition of ##M^{B,\mathcal E}## correctly?)
 
  • #3
i don't think i have understood what you mean :(. could you please do an example of the calculation you have done by using a vector?
 
  • #4
I don't want to give away too much information. We only give hints here, not complete solutions. But I can of course explain what I did there. I just rewrote ##e_i## as a linear combination of the ##v_i## and then I used that ##\phi## is linear.

The ##\phi(v_i)## are easy to calculate, since you've been given the matrix representation of ##\phi## in the ##\{v_i\}## basis.
 
  • #5
thank you :) solved it!
 

Related to LINEAR ALGEBRA: image of vectors through other basis

1. What is the concept of an image of a vector through another basis?

The image of a vector through another basis refers to the representation of a vector in a new coordinate system or basis. This is done by finding the linear combination of basis vectors that would produce the original vector.

2. Why is it important to understand the image of vectors through other basis in linear algebra?

Understanding the image of vectors through other basis is important because it allows for the transformation of vectors from one coordinate system to another. This is crucial in many applications such as computer graphics, machine learning, and engineering.

3. How do you find the image of a vector through another basis?

To find the image of a vector through another basis, you need to first express the vector in terms of the basis vectors of the original coordinate system. Then, using the transformation matrix that maps the original basis to the new basis, you can find the linear combination of the new basis vectors that would produce the original vector.

4. Can the image of a vector through another basis be different from the original vector?

Yes, the image of a vector through another basis can be different from the original vector. This is because the new coordinate system may have different basis vectors and therefore, the linear combination needed to produce the original vector may be different.

5. What is the relationship between the image of a vector through another basis and the change of basis matrix?

The image of a vector through another basis is directly related to the change of basis matrix. The change of basis matrix is used to transform a vector from one coordinate system to another, and the image of a vector through another basis is found by applying this transformation. The change of basis matrix can also be used to find the inverse image, or the original vector, from the image of a vector in the new coordinate system.

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