New to Linear Algebra - LU Decomposition

In summary: Due to the fact that $L^{-1}L_1 = UU_1^{-1}$The resulting matrix must be a diagonal matrix under the circumstances that it must both be a lower-triangular and upper-triangular matrix.Denote $D$ as the desired diagonal matrix.$L^{-1}L_1 = UU_1^{-1} = D$As D is a product of two invertible matrices, D must be invertible as well. Moreover,$L^{-1}L_1 = D$$LL^{-1}L_1=LD$$L_1=LD$$
  • #1
pp123123
6
0
Just came across LU decomposition and I am not sure how to work on this problem:

Let L and L1 be invertible lower triangular matrices, and let U and U1 be invertible upper triangular matrices. Show that LU=L1U1 if and only if there exists an invertible diagonal matrix D such that L1=LD and U1=D-1U. [Hint: Scrutinize L-1L1=UU1-1]

I could work on the part till L-1L1=UU1-1, but I am not sure what I could do further. Give me some hints (and I don't actually know how to prove iff statements)?

Thankss!
 
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  • #2
pp123123 said:
Just came across LU decomposition and I am not sure how to work on this problem:

Let L and L1 be invertible lower triangular matrices, and let U and U1 be invertible upper triangular matrices. Show that LU=L1U1 if and only if there exists an invertible diagonal matrix D such that L1=LD and U1=D-1U. [Hint: Scrutinize L-1L1=UU1-1]

I could work on the part till L-1L1=UU1-1, but I am not sure what I could do further. Give me some hints (and I don't actually know how to prove iff statements)?
In the equation $L^{-1}L_1 = UU_1^{-1}$, the left side is a lower-triangular matrix, and the right side is an upper-triangular matrix. If they are equal then they must represent a matrix that is both lower-triangular and upper-triangular. What can you say about such a matrix?

To prove an iff statement, you must show that the implication works in both directions. In this case, you first need to prove that if $LU = L_1U_1$ then there exists an invertible diagonal matrix $D$ such that $L_1 = LD$ and $U_1 = D^{-1}U$. Then you also have to prove the converse implication, namely that if there exists an invertible diagonal matrix $D$ such that $L_1 = LD$ and $U_1 = D^{-1}U$ then it follows that $LU = L_1U_1$.
 
  • #3
Opalg said:
In the equation $L^{-1}L_1 = UU_1^{-1}$, the left side is a lower-triangular matrix, and the right side is an upper-triangular matrix. If they are equal then they must represent a matrix that is both lower-triangular and upper-triangular. What can you say about such a matrix?

To prove an iff statement, you must show that the implication works in both directions. In this case, you first need to prove that if $LU = L_1U_1$ then there exists an invertible diagonal matrix $D$ such that $L_1 = LD$ and $U_1 = D^{-1}U$. Then you also have to prove the converse implication, namely that if there exists an invertible diagonal matrix $D$ such that $L_1 = LD$ and $U_1 = D^{-1}U$ then it follows that $LU = L_1U_1$.

Oh I get it. So is it okay to write something like:

Due to the fact that $L^{-1}L_1 = UU_1^{-1}$
The resulting matrix must be a diagonal matrix under the circumstances that it must both be a lower-triangular and upper-triangular matrix.
Denote $D$ as the desired diagonal matrix.

$L^{-1}L_1 = UU_1^{-1} = D$
As D is a product of two invertible matrices, D must be invertible as well. Moreover,
$L^{-1}L_1 = D$
$LL^{-1}L_1=LD$
$L_1=LD$

$UU_1^{-1}U_1=DU_1$
$U=DU_1$
$D^{-1}U=D^{-1}DU_1$
$U_1=D^{-1}U$

On the other hand, given $L_1=LD$ and $U_1=D^{-1}U$,
$L_1U_1=LDD^{-1}U$
$L_1U_1=LU$

Thus, the statement is proved.

Much Thanks!
 

Related to New to Linear Algebra - LU Decomposition

1. What is LU decomposition?

LU decomposition is a method used in linear algebra to decompose a square matrix into two triangular matrices, L and U. This method is commonly used to solve systems of linear equations, as it simplifies the process of finding the inverse of a matrix.

2. What is the purpose of LU decomposition?

The purpose of LU decomposition is to simplify the process of solving linear equations. By decomposing a matrix into two triangular matrices, the process of finding the inverse becomes easier and faster. LU decomposition also allows for more efficient matrix operations and can be used in various applications, such as data compression and numerical analysis.

3. How is LU decomposition different from other methods of solving linear equations?

Unlike other methods such as Gaussian elimination or Cramer's rule, LU decomposition does not involve performing row operations on the original matrix. Instead, the matrix is decomposed into two triangular matrices, making it easier to find the inverse and solve the system of equations. Additionally, LU decomposition can be used for sparse matrices, whereas other methods may not be applicable.

4. When should LU decomposition be used?

LU decomposition is typically used when solving systems of linear equations with multiple variables. It can also be used in applications where efficient matrix operations are required, such as in signal processing or image recognition. Additionally, LU decomposition can be used to find the determinant of a matrix and to calculate eigenvalues and eigenvectors.

5. Can LU decomposition be used for non-square matrices?

No, LU decomposition can only be used for square matrices. If a non-square matrix needs to be decomposed, other methods such as QR decomposition or singular value decomposition (SVD) can be used. However, LU decomposition can still be applied to square matrices of any size, making it a versatile method for solving linear equations.

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