Finding the eigenspace for this value of lambda

  • Thread starter ChiralSuperfields
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In summary, the two students were able to solve an equation without inverting a matrix because the equations said the same thing and the determinant of the augmented matrix was 0.
  • #1
ChiralSuperfields
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Homework Statement
Please see below
Relevant Equations
Please see below
For this,
1682889535119.png

I don't understand how they solved,
1682889573062.png

Because we would have to take the inverse of both side which would give the inverse of the matrix ##2 \times 2## matrix on the left hand side which dose not have an inverse.

Dose anybody please know how they did this?

Many thanks!
 
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  • #2
In equation form, we have ##-6 x +6 y =0 \iff x - y = 0## (dividing both sides by -6) for the first line. Likewise for the second line, ##5 x -5 y =0 \iff x - y = 0## (dividing both sides by 5). So both equations say the same thing.
Now you can see what they were doing in matrix form and why there is no need to include ##x## and ##y## where they were manipulating the augmented matrices. It really represents the same thing.
 
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  • #3
There isn't a single solution for the eigenvector(s). That's why you can't invert that matrix. That's how it is with eigenvalue problems. In fact, that's how you find the eigenvalues with the characteristic equation |AI|=0, i.e. find λ that makes AI not invertable.
 
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  • #4
ChiralSuperfields said:
Dose anybody please know how they did this?
Again, that's "does".

Your thread title indicates that you are to find the eigenspace for a matrix. IOW, the set of all nonzero vectors x (in ##\mathbb R^2## here) such that Ax = λx, or equivalently, ##(A - \lambda I)\mathbf x = \mathbf 0##.
In order for x to be nonzero, the determinant of ##A - \lambda I## must be zero.

I'm guessing that your textbook is probably explaining this. Are you skipping over parts of the textbook?
 
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  • #5
Thank you @FactChecker , @DaveE and @Mark44!

I think I understand now :)

@Mark44, yes, sadly, I have to skip over parts of the textbook as the course jumps from one topic to another. Also sorry I did not see the dose again.
 

What is an eigenspace?

An eigenspace is a vector space associated with a specific eigenvalue of a linear transformation. It consists of all the eigenvectors corresponding to that eigenvalue, along with the zero vector.

Why is finding the eigenspace important?

Finding the eigenspace allows us to understand the behavior of a linear transformation and its corresponding eigenvalues. It also helps us solve systems of linear equations and diagonalize matrices.

How do you find the eigenspace?

To find the eigenspace, we first need to find the eigenvalues of the linear transformation. Then, for each eigenvalue, we solve the system of equations (A-λI)x=0, where A is the matrix representing the linear transformation and λ is the eigenvalue. The solutions to this system of equations form the eigenspace for that eigenvalue.

What does it mean to find the eigenspace for a specific value of lambda?

Finding the eigenspace for a specific value of lambda means finding all the eigenvectors associated with that particular eigenvalue. These eigenvectors, along with the zero vector, form the eigenspace.

Can there be more than one eigenspace for a single value of lambda?

Yes, it is possible for there to be multiple eigenspaces for a single eigenvalue. This can happen when the geometric multiplicity (number of linearly independent eigenvectors) is less than the algebraic multiplicity (the number of times the eigenvalue appears as a root of the characteristic polynomial).

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