CHAPTER 10.14: EIGENVALUES AND
EIGENVECTORS: How does one find eigenvalues and eigenvectors numerically? In
this segment we will talk about how to find eigenvalues and eigenvectors
numerically this of course um all of this is an introduction to matrix
algebra course most of the times when you find eigenvalues and eigenvectors
numerically so I just want to give you a flavor of how we do we find it
numerically and one of the methods which is used to find eigenvalues and
eigenvectors is called the Power Method now the power method only finds the
largest eigenvalue in magnitude so if you would know the eigenvalues of a
square matrix and take the absolute value of all of them it will find the one
for which that absolute value is largest it will find only that eigenvalue so
you are going to find the largest eigenvalue magnitude and also it does not
work it does not work for repeated eigenvalues so if the largest eigenvalue
is repeated then it this power method will not work. So
let’s go and see that how this is going to work for us the problem which we
have is that we want to find the eigenvalues and eigenvectors by doing this
[A]=[X] = λ[X] no zero x value so that we can satisfy this set of
equations [A]=[X] = λ[X] λ is the eigenvalue and [X] is the
eigenvector so how do we go about doing this in Euler’s vector is that we
assume a guess [X] so that’s a guess for the eigenvector choose one component
to be unity so what that means is that the guess which you have chosen of
course it has to be a nonzero vector because that’s how the eigenvector is
defined eigenvector has to be a nonzero vector but one of the components of
this vector [X] has to be unity has to be 1 so and then you have to keep it
to be one throughout the whole process so you choose that to be one so once
you have chosen that to be one what you’re going to do is you’re going to find
[Y1] another vector [Y1] which will be found by simply taking the [X] vector
which you just assumed which you use as a guess as your first estimate for an
eigenvector [Y1]=[A][X] so how do you find X1 now you find X1 by saying hey [Y1]=λ[X1] so you have [Y1] which you just found
out so the question is how do I find λ equal to some other vector X1 and
the way you do it is you say by keeping same component to be unity so
whichever component you chose to be one you got to keep the same one to be
one and that’s how you find λ because you have to multiply by scaler so
that the same component becomes unity in this one so what that means is that
you have found the next guess for your eigenvector so now it becomes a
repetition part so repeat steps two and three until convergence so you will
repeat steps two and three until you find out if the λ values your
getting is converging how do you check for convergence your
going to check for convergence by simply( λ1-λi÷λ1)*100 for
example this is going to give you your absolute approximate error between the
current approximation and the previous approximation and what you’re going to
do is you’re going to check whether it is less than pre-specified tolerance so
you might have specified tolerance of .5%,
.25% based on that you will be able to use that as your stopping
criteria so we will look at this whole algorithm through an example and
that’s how you find the eigenvalue of a matrix and this is the end of this
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