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anonymous
 3 years ago
how do we find eigenvectors of any matrix where detA =0?
anonymous
 3 years ago
how do we find eigenvectors of any matrix where detA =0?

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anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0i mean det (A (lamda)I)= 0 say for example A = (1 2;1 4)

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0i know the eigen values are lamda = 3 and lamda =2 but how to find eigenvectors

KingGeorge
 3 years ago
Best ResponseYou've already chosen the best response.1If you have a matrix with a determinant of 0, I'm not sure what to do. However, for your example, let \[A=\begin{bmatrix} 1&2\\1&4\end{bmatrix}\]Then we look at the determinant of \(A\lambda I_2\). So we have \[\det\left(\begin{bmatrix} 1&2\\1&4\end{bmatrix}\begin{bmatrix} \lambda&0\\0&\lambda\end{bmatrix}\right) \\ =\det\left(\begin{bmatrix} 1\lambda&2\\1&4\lambda\end{bmatrix}\right) \\ =(1\lambda)(4\lambda)(2)(1)\]And set this equal to 0.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0yeah i know how to find the eigenvalues completely like i am good at that but it asks what are the eigen vectors thats where i get confused

KingGeorge
 3 years ago
Best ResponseYou've already chosen the best response.1To be honest, I'm not too great at eigenvectors either.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0hmm true they are a pain. like i am reading the theorem you have to sub it back into (A  (lamda)I)x =0 and then solve for x using those lamdas but i reduce my matrix down for both the lamdas and i get x1 = x2 but wth does that mean

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0You've pretty much solved one of the eigenvectors already. You just simplified it too much. x1 = x2 means x1  x2 = 0. What values of x1 and x2 will solve that equation? Eigenvectors are not unique, so there's infinitely many that will work in this case. Then, your eigenvector is [x1 x2]. Note: Eigenvalues are unique, so your eigenvector only corresponds to that eigenvalue, not both. You'll have to plug in the next eigenvalue to get your other eigenvector.

KingGeorge
 3 years ago
Best ResponseYou've already chosen the best response.1So if we have \(\lambda=2,3\), then we have \(A=2I_2 x\) or \(A=3I_2 x\). Let's start with 3. That gives us\[\begin{bmatrix} 1&2\\1&4\end{bmatrix}\begin{bmatrix} x_1\\x_2\end{bmatrix}=3\begin{bmatrix} x_1\\x_2\end{bmatrix}\]Solving for this, we get that \(x_1=x_2\). So I guess that means that your eigenvector for \(\lambda=3\) is just any vector where both \(x_1\) and \(x_2\) are the same.

KingGeorge
 3 years ago
Best ResponseYou've already chosen the best response.1If you solve for \(\lambda=2\) instead, you get that \(x_1=2x_2\). So in this case, your eigenvector for \(\lambda=2\) is any vector where \(x_2\) is twice \(x_1\).

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0hmm ya thats what it said but i wasnt getting the right answer do they use any x values like we can use any number right?

KingGeorge
 3 years ago
Best ResponseYou've already chosen the best response.1You should be able to use any numbers you want, unless I'm grossly misunderstanding eigenvectors.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0Right. So, in the example where lamda is equal to 3, you have x1  x2 = 0. So, [x1 x2] could be [1, 1] or [3,3] or [sqrt(2),sqrt(2)]. The simpler the better though, in my opinion.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0true for some reason they have [1/sqrt2 1/sqrt2] thats where i was going crazy in how they determined that

KingGeorge
 3 years ago
Best ResponseYou've already chosen the best response.1They wanted a normalized vector apparently (length of 1). Anyways, I've got to get going. Good luck.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0ooo alright thanks a lot though

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0but @DanielxAK to get a normalized eigen vector we find the main eigenvector and normalize it?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0Yes, if x is your vector, then to normalize it, you divide it by the norm: x/x. The norm of x (x) is just the distance equation. (So, sqrt(x1^2 + x2^2) = x) Also, note that the normalized eigenvector is unique. That's probably one reason why they're asking you to normalize it.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0oo true probably thanks a lot man, just another question do you know anything about symmetric matrices?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0A bit. What's your question?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0how do we show something is symmetric like i know A^T = A but its talking about some orthagonal stuff where A = CDC^T

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0Can you state the whole question? Is it asking you to show A^T = A when A = CDC^T? I'm guessing C is an orthogonal matrix and D is a diagonal matrix?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0yeahh it isnt a question my TA just said just know something about that because it may be on the test

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0is this like a spectral decomposition?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0Yes, sort of. It's a very specific type of decomposition. Matrices that have certain properties are easier to decompose. Decomposition is important in computing solutions to matrices. Some decompositions are more ideal than others. Anyway, If A = CDC*, then A* = (CDC*)* = (C*)*D*C*= CD*C* = CDC* = A. (D* = D since D is diagonal). Does that make sense?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0i didnt know (C*DC)* = (C*)*D*C* like i thought it was (AB)* = B*A* ?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0Correct. But you have three matrices instead of two. So, in the case of two matrices, you're reversing the order. The same is true for three.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0hmm true do you mind showing me an example of spectral decomposition if you dont mind ?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0i have A = ( 3 6 1; 6 9 4; 1 4 3)

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0I'm not going to work it out by hand. That'd be quite a bit of work for a 3x3 matrix. I did it on Matlab really quick though. So, you'd have it of the form: A = C*D*C' Where, roughly, C = [ 0.4551 0.5802 0.6754 0.8460 0.0451 0.5313 0.2778 0.8132 0.5114] D = [ 13.5417 0 0 0 3.9353 0 0 0 2.4770]

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0ok sounds good and C= the normalized matrix for A?

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0C is your unitary (or orthogonal) matrix. Spectral decomposition is also known as eigenvalue decomposition. If you worked out the eigenvalues of that matrix, you would see that diagonal of D makes up your eigenvalues and each column of C makes up your normalized eigenvectors (which correspond to its eigenvalue). For example, v = 0.4551 0.8460 0.2778 corresponds to the eigenvalue 13.5417.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0ahh okayy i have to keep on practicing thenn i apparently have to do this by hand tommorow lol

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0For a 3x3? I hope not. That's prone to error if done by hand. A 2x2 isn't too bad, but it gets much worse past that.

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0hmm truee just one final question, i have found the eigenvalues its just the vectors that throw me off

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0I have A = (3 1 1; 1 0 2; 1 2 0)

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0and i found the eigenvalues to be lamda1=1, lamda2=4, lamda3 = 2

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0and the question states that. Find the normalized eigenvectors and use them as columns in orthagonal matrix C

anonymous
 3 years ago
Best ResponseYou've already chosen the best response.0Alright. First, build your matrix D using your eigenvalues since you know those. Then, compute the eigenvector for each eigenvalue. Normalize the eigenvectors. Then, to build your C, match the column of C with the diagonal of D. So, the eigenvalue of your first diagonal of D should match your eigenvector which is the first column of C.
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