Find LDL^T Factorization of Matrix: Solve Now!

In summary, the conversation discusses finding the LDL^T factorization of a given matrix through Gaussian elimination. The output of the Gaussian elimination yields an L matrix and a D matrix, but the individual is unsure of their answer as it does not match with the answer in the textbook. They ask for help in identifying where they went wrong in their approach.
  • #1
stunner5000pt
1,461
2
Find the [itex] LDL^T [/itex] factorization of this matrix

[tex] \left(\begin{array}{ccc}{2&-1&0\\-1&2&-1\\0&-1&2\end{array}\right) [/tex]

now i can find the L matrix by gaussian elimination
that yields
[tex]L = \left(\begin{array}{ccc}{1&0&0\\\frac{-2}{3}&1&0\\0&\frac{-1}{2}&1\end{array}\right) [/tex]
[tex] D = \left(\begin{array}{ccc}{\frac{1}{4}&0&0\\0&\frac{1}{3}&0\\0&0&\frac{1}{2}\end{array}\right) [/tex]

i am pretty sure about the ansswer since i checked my working many times.
However this is not the answer at the back of the book! In fact i am not even close!
What am i doing wrong?? Can anyone please help me iwth this?
Thank you for your help!
 
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  • #2
It's hard to tell what you did wrong when you did wrong when you don't tell us what you did! I did a quick "column reduction" to get L and didn't get any like you got.
 
  • #3
well i got those answers by Gaussian Elimination
this is what i did

[tex] \left(\begin{array}{ccc}{2&-1&0\\-1&2&-1\\0&-1&2\end{array}\right) [/tex]

R3 + 2R2
[tex] \left(\begin{array}{ccc}{2&-1&0\\-2&3&0\\0&-1&2\end{array}\right) [/tex]

R2+3R1
[tex] \left(\begin{array}{ccc}{4&0&0\\-2&3&0\\0&-1&2\end{array}\right) [/tex]

and my textbook says that that the D matrix is formed by dividing the square terms of the lower matrix formed and multiply that by the elementary matrix yielding
[tex] D = \left(\begin{array}{ccc}{\frac{1}{4}&0&0\\0&\frac{ 1}{3}&0\\0&0&\frac{1}{2}\end{array}\right) [/tex]
 
  • #4
can anyone tell me what i have done wrong? my answer is not even close to the tedxxt book's answer. However all my steps with the row reductions are correct, as you can see.

I was told that i was not supposed to use row reduction to get the lower matrix? SO what do i do then?
 

1. What is LDL^T factorization of a matrix?

LDL^T factorization is a type of matrix decomposition that expresses a given matrix as the product of a lower triangular matrix (L), a diagonal matrix (D), and the transpose of a lower triangular matrix (L^T). It is commonly used in numerical linear algebra for solving systems of linear equations and computing determinants and inverses.

2. How is LDL^T factorization different from other matrix factorization methods?

Unlike other matrix factorization methods, such as LU or QR factorization, LDL^T factorization is specifically designed for symmetric matrices. This means that it can only be applied to matrices that are equal to their own transpose. Additionally, in LDL^T factorization, the diagonal matrix D contains only positive values, making it easier to compute inverses and determinants.

3. What are the benefits of using LDL^T factorization?

One of the main benefits of LDL^T factorization is that it reduces the computational complexity of solving systems of linear equations. This is because the lower triangular matrices L and L^T are sparse, meaning they contain mostly zeros. This sparsity allows for more efficient algorithms to be used in the computation process. Additionally, LDL^T factorization is numerically stable, meaning it produces accurate results even with small rounding errors.

4. How do you perform LDL^T factorization on a matrix?

To perform LDL^T factorization on a matrix, you first need to ensure that the matrix is symmetric. Then, you can use a variety of algorithms, such as the Cholesky decomposition, to compute the lower triangular matrix L and the diagonal matrix D. Once these matrices have been calculated, the LDL^T factorization can be expressed as A = LDU^T, where U is the transpose of L.

5. In what applications is LDL^T factorization commonly used?

LDL^T factorization is commonly used in applications that involve solving systems of linear equations, such as in engineering, physics, and computer science. It is also used in computing determinants and inverses of symmetric matrices, which has applications in statistics and data analysis. Additionally, LDL^T factorization is used in numerical methods for solving differential equations and optimization problems.

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