LU Decomposition | Engineering Mathematics - Civil Engineering (CE) PDF Download

L U Decomposition of a System of Linear Equations

L U decomposition of a matrix is the factorization of a given square matrix into two triangular matrices, one upper triangular matrix and one lower triangular matrix, such that the product of these two matrices gives the original matrix. It was introduced by Alan Turing in 1948, who also created the turing machine.

This method of factorizing a matrix as a product of two triangular matrices has various applications such as solution of a system of equations, which itself is an integral part of many applications such as finding current in a circuit and solution of discrete dynamical system problems; finding the inverse of a matrix and finding the determinant of the matrix.
Basically, the L U decomposition method comes handy whenever it is possible to model the problem to be solved into matrix form. Conversion to the matrix form and solving with triangular matrices makes it easy to do calculations in the process of finding the solution.

A square matrix A can be decomposed into two square matrices L and U such that A = L U where U is an upper triangular matrix formed as a result of applying Gauss Elimination Method on A; and L is a lower triangular matrix with diagonal elements being equal to 1.

For A =  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  , we have L =   LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  and U =   LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  ; such that A = L U.

Gauss Elimination Method
According to the Gauss Elimination method:
1. Any zero row should be at the bottom of the matrix.
2. The first non zero entry of each row should be on the right hand side of the first non zero entry of the preceding row. This method reduces the matrix to row echelon form.

Steps for L U Decomposition
Given a set of linear equations, first convert them into matrix form A X = C where A is the coefficient matrix, X is the variable matrix and C is the matrix of numbers on the right hand side of the equations.

Now, reduce the coefficient matrix A , i.e., matrix obtained from the coefficients of variables in all the given equations such that for ‘n’ variables we have an nXn matrix, to row echelon form using Gauss Elimination Method. The matrix so obtained is U.

To find L, we have two methods. The first one is to assume the remaining elements as some artificial variables, make equations using A = L U and solve them to find those artificial variables.
The other method is that the remaining elements are the multiplier coefficients because of which the respective positions became zero in the U matrix. (This method is a little tricky to understand by words but would get clear in example below)

Now, we have A (the nXn coefficient matrix), L (the nXn lower triangular matrix), U (the nXn upper triangular matrix), X (the nX1 matrix of variables) and C (the nX1 matrix of numbers on the right hand side of the equations).

The given system of equations is A X = C. We substitute A = L U. Thus, we have L U X = C.
We put Z = U X, where Z is a matrix or artificial variables and solve for L Z = C first and then solve for U X = Z to find X or the values of the variables, which was required.

Example:
Solve the following system of equations using LU Decomposition method:

LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

Solution: Here, we have

LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  such that A X = C.

Now, we first consider  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  and convert it to row echelon form using Gauss Elimination Method.

So, by doing   R2→R2 - 4R...................................................(1)
                          R3→R3 - 3R1....................................................(2)

we get   LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

R3→R- (- 2) R2.........................................................(3)

we get  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

(Remember to always keep ‘ – ‘ sign in between, replace ‘ + ‘ sign by two ‘ – ‘ signs)

Hence, we get L =  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  and U =  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  

(notice that in L matrix,  LU Decomposition | Engineering Mathematics - Civil Engineering (CE) is from (1),  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  is from (2) and  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  is from (3))

Now, we assume Z =  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)   and solve L Z = C.

LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

Now, we solve U X = Z

LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

Therefore, we get  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

Thus, the solution to the given system of linear equations is  LU Decomposition | Engineering Mathematics - Civil Engineering (CE)  and hence the matrix X =   LU Decomposition | Engineering Mathematics - Civil Engineering (CE)

The document LU Decomposition | Engineering Mathematics - Civil Engineering (CE) is a part of the Civil Engineering (CE) Course Engineering Mathematics.
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FAQs on LU Decomposition - Engineering Mathematics - Civil Engineering (CE)

1. What is LU decomposition?
Ans. LU decomposition, also known as LU factorization, is a method used in numerical analysis to factor a matrix as the product of a lower triangular matrix (L) and an upper triangular matrix (U). It is often used to simplify solving systems of linear equations, as it reduces the complexity of matrix operations.
2. How does LU decomposition work?
Ans. LU decomposition works by decomposing a given matrix A into the product of two matrices, L and U. The matrix L contains the lower triangular elements, with ones on the diagonal, and the matrix U contains the upper triangular elements. The process involves applying row operations to transform the original matrix into an upper triangular form, while keeping track of the operations in matrix L.
3. What are the advantages of using LU decomposition?
Ans. LU decomposition offers several advantages in solving systems of linear equations. Firstly, it allows for efficient computation of determinants and matrix inverses. Secondly, once a matrix is factored, solving multiple systems with the same coefficient matrix becomes computationally efficient. Additionally, LU decomposition can be used to identify the rank of a matrix and provide insights into its properties.
4. Can LU decomposition be used for all types of matrices?
Ans. No, LU decomposition can only be used for square matrices that are non-singular, meaning they have a non-zero determinant. If a matrix is singular or non-square, LU decomposition cannot be directly applied. In such cases, alternative methods like QR decomposition or singular value decomposition (SVD) may be used.
5. Are there any limitations or challenges associated with LU decomposition?
Ans. Yes, there are a few limitations and challenges associated with LU decomposition. One limitation is that it can be computationally intensive for large matrices, requiring significant memory and processing power. Another challenge is that LU decomposition may not be numerically stable for matrices with small pivots or ill-conditioned systems. In such cases, techniques like pivoting can be used to enhance stability.
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