LU decomposition of matrix

In summary, the conversation discusses LU decomposition of a matrix and the requirement for the principal diagonal elements of either L or U to be unity. The Wikipedia article states that the factorization is unique if this requirement is met. The person asking the question mentions that most algorithms found online have L with unity diagonal elements, but their problem requires U to have unity diagonal elements. They share a file for review and correction.
  • #1
Muthuraj R
9
0
Actually I am new to this topic.
I read few tutorials about LU decomposition of matrix in the net.
A = LU ; A - actual matrix, L - Lower triangular matrix, U - Upper triangular matrix.Few people say that, principal diagonal elements of L should be unity.
Some others say that, principal diagonal elements of U should be unity.
Pls clarify.
or else suggest me some suitable method for LU decomposition of a matrix.
Thanks.
 
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  • #3
Thanks, chiro.
Wikipedia says,
"The factorization is unique if we require that the diagonal of L (or U) consist of ones."
Fine. Either 'L' or 'U' has to have unity diagonal elements.
Algorithms for LU decomposition that I can see in the net follows 'L' to have unity diagonal elements. But my problem requires 'U' to have unity diagonal elements.
Could pls suggest such algorithm.
 
  • #4
Ya. Got it.
Attached that file.
Pls check and correct me if anything is wrong.
Thanks all.
 

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  • #5


Hi there, don't worry if you are new to this topic, it can be complex and confusing at first. LU decomposition is a method used to factorize a matrix into a lower triangular matrix (L) and an upper triangular matrix (U). This can be useful in solving systems of linear equations and in other mathematical operations. The principal diagonal elements of L and U do not necessarily have to be unity, but it is common to choose them to be unity for simplicity and convenience. The important thing is that the product of L and U should equal the original matrix A.

As for a suitable method for LU decomposition, there are a few options such as Gaussian elimination, Crout's method, and Doolittle's method. It is best to choose a method that suits your specific problem and data. I suggest you continue to study and practice with different methods to gain a better understanding of LU decomposition. Good luck!
 

What is LU decomposition of matrix?

LU decomposition of matrix is a method used to factorize a square matrix into two triangular matrices, lower and upper, such that when the two matrices are multiplied together, they result in the original matrix. This method is used to solve systems of linear equations and is commonly used in numerical analysis and computer science applications.

What is the difference between LU decomposition and Gaussian elimination?

The main difference between LU decomposition and Gaussian elimination is that in LU decomposition, the original matrix is decomposed into two triangular matrices, while in Gaussian elimination, the original matrix is transformed into a row-echelon form. LU decomposition is generally more efficient as it allows for faster solving of multiple linear systems with the same coefficient matrix.

What are the advantages of using LU decomposition over other methods?

One of the main advantages of using LU decomposition is that it allows for faster solving of systems of linear equations compared to other methods such as Gaussian elimination. It also allows for the efficient computation of the inverse and determinant of a matrix. Additionally, LU decomposition is less prone to rounding errors compared to other methods.

What is the role of pivoting in LU decomposition?

Pivoting is a technique used in LU decomposition to prevent division by zero and reduce the effects of rounding errors. It involves swapping rows of the original matrix to create a more stable decomposition. Pivoting is especially important when dealing with matrices that have a small pivot element or elements that are close to zero.

Can LU decomposition be used for non-square matrices?

No, LU decomposition can only be used for square matrices, as it involves the factorization of a matrix into two triangular matrices. For non-square matrices, other methods such as QR decomposition or singular value decomposition (SVD) can be used instead.

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