QR factorization- Eigen values

In summary, the conversation is about the process of computing Eigen values for a real symmetric matrix using the QR algorithm. The person is using various resources such as wiki, Numerical Recipes book and other web articles for reference. They are also taking a self-study course in Linear Algebra to improve their skills and understand the topic better. The planned algorithm involves carrying out Householder transformations to obtain a tridiagonal matrix, followed by obtaining Q and R matrices through QR factorization. The person is unsure about how to get Eigen values using this method and is seeking a good reference for help.
  • #1
svishal03
129
1
I'm attempting to write a code for computing the Eigen values of a real symmetric matrix and I'm using the QR algorithm.I'm referring wiki,Numerical Recipees book and other web serach articles.

This is a part of the self-study course I'm doing in Linear Algebra to upgrde my skills.

My aim is not only getting the algorithm but also understanding Linear algebra and this site is a great help.

As I conclude, following algorithm is being planned by me for implemantation:

1. First and foremost carry out Householder transformation to obtain a tridiagonal matrix from (n-2) householder iterations where n is the size of the square symmetric matrix.

2. During each of the above n-2 iterations, we have Q1,Q2,Q3…..Q(n-2) Householdr matrices


3. We can now obtain Q and R (of QR factorization) where Q is an orthogonal matrix and R is an upper triangular matrix

4. R = Qn-2* Qn-1*……….*Q2*Q1

5. Q = Q1*Q2*………*Qn-2

6. Thus we decompose the original matrix A into A = QR

Am I right above?

I'm not very clear how to get Eigen values following this.Can anyone site a good refernce?

Vishal
 
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  • #2
Please can anyone help, it is very urgent..
 

1. What is QR factorization?

QR factorization is a mathematical process used to decompose a matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). It is used in various applications, including solving systems of equations and finding eigenvalues and eigenvectors of a matrix.

2. How is QR factorization related to eigenvalues?

QR factorization is used to find the eigenvalues of a matrix by first decomposing the matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). The eigenvalues can then be found from the diagonal elements of the upper triangular matrix.

3. What are the benefits of using QR factorization to find eigenvalues?

Using QR factorization to find eigenvalues is beneficial because it is a more efficient and numerically stable method compared to traditional methods such as the power iteration method. It also allows for the computation of all eigenvalues of a matrix, not just the dominant one.

4. How is QR factorization computed?

QR factorization can be computed using several methods, including the Gram-Schmidt process and the Householder transformation. These methods involve a series of operations on the original matrix to decompose it into an orthogonal matrix and an upper triangular matrix.

5. Can QR factorization be used for non-square matrices?

Yes, QR factorization can be used for non-square matrices. However, the resulting orthogonal matrix will not be square and the upper triangular matrix will have dimensions that depend on the original matrix's dimensions. This method is often used for solving over- and under-determined systems of equations.

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