Linear algebra: orthonormal basis

In summary, the question is asking to find an orthonormal basis "C" in ##\mathbb{E}^3## formed by eigenvectors of ##\phi##, given the endomorphism ##\phi## and its associated matrix in a specific basis. The solution involves finding the eigenvalues and using them to find a set of eigenvectors, which can then be used to construct an orthonormal basis.
  • #1
Felafel
171
0

Homework Statement


##\phi## is an endomorphism in ##\mathbb{E}^3## associated to the matrix
(1 0 0)
(0 2 0) =##M_{\phi}^{B,B}##=
(0 0 3)

where B is the basis: B=((1,1,0),(1,-1,0),(0,0,-1))

Find an orthonormal basis "C" in ##\mathbb{E}^3## formed by eigenvectors of ##\phi##

The Attempt at a Solution



Being the eigenvalues the elements of the diagonal 1, 2, 3
Aren't (1, 0, 0), (0,2,0), (0,0,3) three orthonormal vectors already?

Or should I write the endomorphism according to the canonical basis first and find new values?
 
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  • #2
Felafel said:

Homework Statement


##\phi## is an endomorphism in ##\mathbb{E}^3## associated to the matrix
(1 0 0)
(0 2 0) =##M_{\phi}^{B,B}##=
(0 0 3)

where B is the basis: B=((1,1,0),(1,-1,0),(0,0,-1))

Find an orthonormal basis "C" in ##\mathbb{E}^3## formed by eigenvectors of ##\phi##

The Attempt at a Solution



Being the eigenvalues the elements of the diagonal 1, 2, 3
Aren't (1, 0, 0), (0,2,0), (0,0,3) three orthonormal vectors already?
Yes, they are, but these aren't the vectors they're asking for.
Felafel said:
Or should I write the endomorphism according to the canonical basis first and find new values?

Use the eigenvalues to find a basis of eigenvectors, and then make an orthonormal basis out of that set of vectors.
 

Related to Linear algebra: orthonormal basis

1. What is an orthonormal basis in linear algebra?

An orthonormal basis in linear algebra is a set of vectors in a vector space that are both orthogonal (perpendicular) to each other and have a length of 1 (unit vectors). This means that they are independent and form a basis for the vector space, allowing for easy calculations and transformations.

2. How do you determine if a set of vectors is an orthonormal basis?

To determine if a set of vectors is an orthonormal basis, you can use the Gram-Schmidt process. This involves finding the orthogonal projection of each vector onto the span of the previous vectors and then normalizing the resulting vectors to have a length of 1. If the resulting set of vectors is independent, then it is an orthonormal basis.

3. What are the benefits of using an orthonormal basis in linear algebra?

Using an orthonormal basis in linear algebra has several benefits, including simplifying calculations and transformations, making it easier to find solutions to systems of equations, and providing a geometric interpretation of vector spaces.

4. Can you have more than one orthonormal basis for a vector space?

Yes, it is possible to have multiple orthonormal bases for a vector space. This is because there are infinite ways to choose a set of orthogonal and unit vectors that can form a basis for a vector space. However, all orthonormal bases for a given vector space will have the same number of vectors.

5. How is an orthonormal basis used in applications outside of linear algebra?

An orthonormal basis has many applications outside of linear algebra, including in fields such as signal processing, image processing, and quantum mechanics. It is also commonly used in computer graphics to represent rotations and transformations in 3D space. Additionally, machine learning algorithms often use orthonormal bases to reduce the dimensionality of data and improve accuracy.

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