Finding a vector A from given eigen values and eigenvectors

In summary, a matrix A with eigenvectors [2,1] and [1,-1] and eigenvalues 2 and -3 respectively, can be used to find the product Ab for a given vector b = [1,1]. By rewriting b as a linear combination of the eigenvectors, A can then be solved for using the equation A[1,1] = 2/3 A[2,1] -1/3 A[1,-1] and the values for the eigenvectors and eigenvalues.
  • #1
sg001
134
0

Homework Statement



A matrix A has eigenvectors [2,1] [1,-1]
and eigenvalues 2 , -3 respectively.

Determine Ab for the vector b = [1,1].


Homework Equations





The Attempt at a Solution



First I put be as a combination of the two eigenvectors
ie

2/3[2,1] -1/3[1,-1] = b

so A(2/3[2,1] -1/3[1,-1]) = Ab

but not sure what to do from this point as the sltn says it went from this

A(2/3[2,1] -1/3[1,-1]) = (2[2,1] -1/3[1,-1]) but I am not sure how??
 
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  • #2
hi sg001! :smile:
sg001 said:
A matrix A has eigenvectors [2,1] [1,-1]
and eigenvalues 2 , -3 respectively.

A(2/3[2,1] -1/3[1,-1]) = (2[2,1] -1/3[1,-1]) but I am not sure how??

no, that can't be right :redface:

if the second eigenvalue is -3, that factor must be -3, not -1/3
 
  • #3
tiny-tim said:
hi sg001! :smile:


no, that can't be right :redface:

if the second eigenvalue is -3, that factor must be -3, not -1/3

hmm

they have the answer of Ab = 1/3[11,1] ?

is this correct?
 
  • #4
you mean 1/3[11,7] ?

then the question must be wrong, the eigenvalue must be -1/3
 
  • #5
tiny-tim said:
you mean 1/3[11,7] ?

then the question must be wrong, the eigenvalue must be -1/3

no, that's the sltn and the exact question they give,,,

so how would you approach this type of question then..

rewrite b as a liner combination of the given eigenvalues... then how do you solve for A from here...

2/3[2,1] -1/3[1,-1] = b

just so I know if it comes up in my test.

Thanks for the help.
 
  • #6
hi sg001! :smile:

you want to find A[1,1].

start with [1,1] = 2/3[2,1] -1/3[1,-1]

so A[1,1] = 2/3 A[2,1] -1/3 A[1,-1]

= 2/3 2[2,1] -1/3 -3[1,-1]


= [11/3,1/3] …

oh that is right!

(i shouldn't have tried doing it in my head :redface:)

the important step is the bit in bold :wink:
 
  • #7
tiny-tim said:
hi sg001! :smile:

you want to find A[1,1].

start with [1,1] = 2/3[2,1] -1/3[1,-1]

so A[1,1] = 2/3 A[2,1] -1/3 A[1,-1]

= 2/3 2[2,1] -1/3 -3[1,-1]


= [11/3,1/3] …

oh that is right!

(i shouldn't have tried doing it in my head :redface:)

the important step is the bit in bold :wink:

cool thanks
 

FAQ: Finding a vector A from given eigen values and eigenvectors

How do I find the vector A from given eigenvalues and eigenvectors?

To find the vector A, you can use the formula A = PDP^-1, where P is the matrix of eigenvectors and D is the diagonal matrix of eigenvalues. Alternatively, you can also use the formula A = λx, where λ is an eigenvalue and x is the corresponding eigenvector.

What is the significance of finding the vector A from given eigenvalues and eigenvectors?

Finding the vector A allows you to decompose a matrix into its eigenvalues and eigenvectors, which can provide useful insights into the behavior and properties of the matrix. It also allows you to easily calculate powers of the matrix and solve differential equations involving the matrix.

Can I find the vector A for any matrix with given eigenvalues and eigenvectors?

Yes, as long as the matrix is diagonalizable (meaning it has a full set of linearly independent eigenvectors), you can use the formulas mentioned in question 1 to find the vector A.

Is finding the vector A the same as diagonalizing a matrix?

Yes, finding the vector A from given eigenvalues and eigenvectors is equivalent to diagonalizing a matrix. This is because the diagonalization process involves finding the eigenvectors and eigenvalues of a matrix, and then using them to construct a diagonal matrix.

What are some real-world applications of finding the vector A from given eigenvalues and eigenvectors?

Finding the vector A is commonly used in many fields such as physics, engineering, and economics. It can be used to analyze the stability of dynamic systems, solve differential equations, and uncover hidden patterns and relationships in data. It is also used in image and signal processing to compress and denoise data.

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