Linear Algebra: LU Decomposition

In summary: I figured that would be the case but was just wondering why that was the case.Oh I see, thank you for your reponse. I figured that would be the case but was just wondering why that was the case.
  • #1
Master1022
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Homework Statement
Find the LU Decomposition of the matrix below
Relevant Equations
M = LU
Here is the initial matrix M:
[tex] M = \begin{bmatrix} 3 & 1 & 6 \\ -6 & 0 & -16 \\ 0 & 8 & -17 \end{bmatrix} [/tex]

I have used the shortcut method outlined in this youtube video: LU Decomposition Shortcut Method.

Here are the row reductions that I went through in order to get my U matrix:
1. [itex] R_3 - 8 R_1 [/itex]
[tex] = \begin{bmatrix} 3 & 1 & 6 \\ -6 & 0 & -16 \\ -24 & 0 & -65 \end{bmatrix} [/tex]
2. [itex] R_3 - 4 R_2 [/itex]
[tex] = \begin{bmatrix} 3 & 1 & 6 \\ -6 & 0 & -16 \\ 0 & 0 & -1 \end{bmatrix} [/tex]
3. [itex] R_2 + 2 R_1 [/itex]
[tex] U = \begin{bmatrix} 3 & 1 & 6 \\ 0 & 2 & -4 \\ 0 & 0 & -1 \end{bmatrix} [/tex]

This yields the correct U matrix, however, I get a slightly different L matrix to the answer. My L matrix is:
[tex] L = \begin{bmatrix} 1 & 0 & 0 \\ -2 & 1 & 0 \\ 4 & 8 & 1 \end{bmatrix} [/tex]

In the answer, the final row reads 0, 4, 1.

Why would this be the case?

Any help is greatly appreciated.
 
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  • #2
Master1022 said:
Why would this be the case?
Have you checked that M=LU?
 
  • #3
DrClaude said:
Have you checked that M=LU?

Thanks for your response. I figured that it doesn't (my LU doesn't equal M) if my answer is wrong. However, I was wondering why using that method seemed to lead me to the wrong answer?
 
  • #4
Master1022 said:
Thanks for your response. I figured that it doesn't (my LU doesn't equal M) if my answer is wrong. However, I was wondering why using that method seemed to lead me to the wrong answer?
That's how to say because you presented only how you got U, not L.
 
  • #5
DrClaude said:
That's how to say because you presented only how you got U, not L.

Sure, the way I got L was by looking at the row reduction reduction used to turn a given element into a 0 (e.g. [itex] R_1 - 4 R_2 [/itex]) and placing the opposite of the multiplier (e.g. we had -4, so we put +4) in the corresponding place in the L matrix.

For my L matrix, step 1 led to element (3,2); step 2 led to element (3,1); step 3 led to element (2,1).
 
  • #6
The method presented in the video only works if you go systematically, eliminating first the zero in the first column of the second row, and so on. Otherwise, you need to keep track of all the transformation steps.
 
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  • #7
DrClaude said:
The method presented in the video only works if you go systematically, eliminating first the zero in the first column of the second row, and so on. Otherwise, you need to keep track of all the transformation steps.
Oh I see, thank you for your reponse.
 

1. What is LU decomposition?

LU decomposition is a method used in linear algebra to factor a matrix into the product of a lower triangular matrix (L) and an upper triangular matrix (U). This decomposition is useful for solving systems of linear equations and calculating determinants.

2. How is LU decomposition different from other matrix factorization methods?

Unlike other matrix factorization methods, such as QR decomposition or Cholesky decomposition, LU decomposition does not require the matrix to be square or symmetric. It can be applied to any m x n matrix, where m is the number of rows and n is the number of columns.

3. What is the purpose of LU decomposition?

The main purpose of LU decomposition is to simplify the process of solving systems of linear equations. By factoring a matrix into lower and upper triangular matrices, the system can be solved more efficiently and accurately. LU decomposition is also useful for calculating determinants and inverting matrices.

4. How is LU decomposition calculated?

LU decomposition is typically calculated using Gaussian elimination, a method that involves performing a series of row operations on the original matrix until it is transformed into an upper triangular matrix. The resulting triangular matrix is then used to solve the system of equations.

5. What are the applications of LU decomposition?

LU decomposition has many applications in various fields, including engineering, physics, economics, and computer science. It is commonly used in solving systems of linear equations, calculating determinants and inverses, and in machine learning algorithms such as principal component analysis and singular value decomposition.

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