How to Determine if a Linear Operator is a Symmetry or an Orthogonal Projection?

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In summary: The matrix with respect to the calculated base must have the form of the orthogonal projection or of the symmetric matrix.
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Jimmy84
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Homework Statement

A linear operator given (a matrix). That could be an orthogonal protection (that goes through the origin) or a symmetry with respect to a plane (that goes through the origin).

1-Get the eigenvalues of linear operator
2-Get the eigenspace associated with each eigenvalue.

3-Based on the previous calculations determine if the Operator is a symmetry or an orthogonal protection.

4-Describe an ortogonal base of the given plane, and complete it with a base of R^2
The matrix with respect to the calculated base must have the form of the orthogonal projection or of the symmetric matrix

100 or 100
010 010
00-1 000

Homework Equations


The Attempt at a Solution



I got the eigenvalues 1 and 0 therefore I'm assuming the operator is an orthogonal projection.

I got the eigenvectors

How can I start to do 4?

Im thinking about using gram schidt to get 3 ortogonal vectors and then to use them as a base .

Thanks a lot for any help, I appreciate it.
 
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  • #2
What is the given operator? ehild
 
  • #3
ehild said:
What is the given operator? ehild

It is the matrix
2 1 -1
-1 0 1
1 1 0
 
  • #4
Okay, so this is projection onto a line? What is that line? What is a vector in the direction of that line?
 
  • #5
HallsofIvy said:
Okay, so this is projection onto a line? What is that line? What is a vector in the direction of that line?

It seems to be is an orthogonal projection on a plane that goes through the origin.

For now I am confused, I was thinking about using the set of eigenbasis or eigenvectors of the linear operator B . and to use gram schmidt to get 3 orthogonal bases w1, w2 and w3

Im considering to evaluate w1 w2 and w3 with respect to the basis B to get the projection of the plane with respect to B.
 
  • #6
Once again, what are the eigenvectors corresponding to each eigenvalue?
 
  • #7
the eigenvectors associated with the eigenvalue 1 are -1,1,0 and 1 0 1 .
the ones associated with the eigenvalue 0 are 1 -1 1
 
  • #8
Any linear combination of the eigenvectors belonging to 1 is also an eigenvector to λ=1. Find a combination of a=(1,0,1) and b=(-1,1,0) c=a+kb so the dot product a˙c=0 and choose a and c as orthogonal base in the plane.

ehild
 

FAQ: How to Determine if a Linear Operator is a Symmetry or an Orthogonal Projection?

What is an eigenvalue?

An eigenvalue is a scalar value that represents the magnitude of a vector in a linear transformation. It is a characteristic of a matrix and is found by solving the characteristic equation of the matrix.

How is the eigenvalue of a matrix determined?

The eigenvalue of a matrix can be determined by finding the roots of the characteristic equation, which is det(A-λI)=0, where A is the matrix, λ is the eigenvalue, and I is the identity matrix. The eigenvalues of a matrix can also be determined by using numerical methods such as the power method or QR algorithm.

What is the significance of eigenvalues?

Eigenvalues have many practical applications in mathematics, physics, and engineering. They can help determine stability and convergence in systems, as well as provide information about the behavior of a transformation or matrix. They are also used in data analysis and image processing.

What is the relationship between eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are closely related, as eigenvectors are the associated vectors to each eigenvalue. They are found by solving the system of equations (A-λI)x=0, where x is the eigenvector and λ is the eigenvalue. Eigenvectors are important because they represent the directions in which a linear transformation stretches or compresses.

Can a matrix have complex eigenvalues?

Yes, a matrix can have complex eigenvalues. This is often the case when dealing with higher dimensional matrices or matrices with complex coefficients. Complex eigenvalues and eigenvectors can still provide important information about the behavior of the matrix, such as rotation and scaling.

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