Numerical approximation of the eigenvalues and the eigenvector

In summary, the conversation discusses a problem that guides the steps to obtain a numerical approximation of the eigenvalues and eigenvectors of matrix A using a specific example. This involves defining two sequences of vectors and performing iterations to converge to an eigenvector. The conversation also mentions using a method learned in class to find the eigenvectors and comparing the results.
  • #1
junsugal
6
0

Homework Statement



This problem will guide you through the steps to obtain a numerical approximation of the eigenvalues, and eigenvectors of A using an example.

We will define two sequences of vectors{vk} and {uk}
(a) Choose any vector u [itex]\in[/itex] R2 as u0
(b) Once uk has been determined, the vectors vk+1 and uk+1 are determined as follows:
i. Set vk+1 = Auk
ii. Find the entry of maximum absolute value of vk+1 let's say it is the j-th entry of vk+1
iii. Set uk+1 = vk+1/vjk+1
(c) The sequence {Uk} will converge to an eigenvector for A.

Let A =
2 1
1 2
Use the above method to approximate an eigenvector for A using only 4 iterations, that is finding v5.
Find the eigenvectors for A using the method learned in class and compare.

Homework Equations





The Attempt at a Solution



I assume u0 is (1,1)
From A, i found the eigenvalues which is 3 and 1.
And the corresponding eigenvectors is (1,1) and (-1,1)
Then i get uk as (3k-1, 3k+1)

Now, to the next step, I get my vk+1 as ( 2*(3k-1)+3k+1, 3k-1 + 2*(3k+1))
Plugging in k as 4 as I want to get v5
I get v5 as (242, 244)
And from this step the instruction said to find the entry of maximum absolute value of vk+1 and I don't know how. I am not even sure if what I've done is correct.

Please correct me if I am wrong and please explain on what to do next.
Thanks in advance!
 
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  • #2
You chose u_0=(1,1), you would get u_k=(3^k,3^k). Even if you got v_5=(242,244) somehow, the max abs entry is obviously 244, because |244|>|242|. So u_5=(242/244,1).
Now find another less clever and more random u_0 and try again
 

Related to Numerical approximation of the eigenvalues and the eigenvector

What is the purpose of numerical approximation of eigenvalues and eigenvectors?

The purpose of numerical approximation of eigenvalues and eigenvectors is to estimate the values of these important mathematical concepts when it is not possible to obtain exact solutions analytically. This is often the case for large or complex systems that cannot be easily solved by hand.

What methods are commonly used for numerical approximation of eigenvalues and eigenvectors?

There are several methods that can be used for numerical approximation of eigenvalues and eigenvectors, including the power method, QR algorithm, and inverse iteration. Each method has its own advantages and is suitable for different types of matrices.

How accurate are numerical approximations of eigenvalues and eigenvectors?

The accuracy of numerical approximations of eigenvalues and eigenvectors depends on the method used and the size and complexity of the matrix. In most cases, the approximations will have a small error compared to the exact values, but it is important to test and validate the results to ensure their accuracy.

What challenges are involved in numerical approximation of eigenvalues and eigenvectors?

One of the main challenges in numerical approximation of eigenvalues and eigenvectors is choosing the appropriate method for a given matrix. Some methods may be more efficient or accurate for certain types of matrices, and it is important to understand the properties of the matrix in order to select the best method.

How are numerical approximations of eigenvalues and eigenvectors used in real-world applications?

Numerical approximations of eigenvalues and eigenvectors have many practical applications in fields such as physics, engineering, and data analysis. They are used to solve problems involving large data sets, predict the behavior of complex systems, and optimize processes. These approximations are also used in computer graphics and image processing to manipulate and analyze images.

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