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 one year ago
how do you find a matrix A given its eigenvectors and eigenvalues?? please helpp Dx
 one year ago
how do you find a matrix A given its eigenvectors and eigenvalues?? please helpp Dx

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JamesJ
 one year ago
Best ResponseYou've already chosen the best response.0By definition, a vector v is an eigenvector of matrix A with eigenvalue \( \lambda \) if \[ Av = \lambda v \] Hence \[ (A  \lambda I)v = 0 \] and this is equivalent to saying \[ \det(A  \lambda I) = 0 \] This is therefore the equation you solve to find the eigenvalues. Once you have found the eigenvalues, \( \lambda_i \), you want to find the null space of the operator \[ A  \lambda_i I \] for each \( i \). This corresponds to the eigenspace for \( \lambda_i \). This all probably seems a bit abstract, but is absolutely correct. I STRONGLY recommend you go through you lecture notes and/or text book to find some worked examples. Once you understand them, attempt your particular problem.

missylulu
 one year ago
Best ResponseYou've already chosen the best response.0ok thx for the advice !

missylulu
 one year ago
Best ResponseYou've already chosen the best response.0@JamesJ i can get to the part where you use matrix multiplication to get like 4 separate equations..2 with a, b and 2 with c, d...do you just solve the 4 unknowns and put it in as the matrix??sorry

JamesJ
 one year ago
Best ResponseYou've already chosen the best response.0You want to solve for lambda

JamesJ
 one year ago
Best ResponseYou've already chosen the best response.0If you look at an example, you would see that

missylulu
 one year ago
Best ResponseYou've already chosen the best response.0yeah but in this problem its given so do you just work backwards to get the original matrix?

JamesJ
 one year ago
Best ResponseYou've already chosen the best response.0Then for each eigenvector Av = lambda.v Let v1, v2, ..., vn be basis vectors for each of the eigenspaces. If we treat each of those as column vectors, then you can see that A [ v1 v2 ... vn ] = [ lambda1.v1 lambda2.v2 .... lambdan.vn ] where here lambdaj is the eigenvector corresponding to vj. Call this matrix V = [ v1 v2 ... vn ] Then V^1 A V = V^1 [ lambda1.v1 lambda2.v2 .... lambdan.vn ] = D where D is a diagonal matrix with the lambdaj down the diagonal. Hence A = V D V^1 So that's how you recover A from the lambda and the vj. Again, read an example!
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