Triangular Decomposition Method In 3 × 3 Matrices

 

There are several ways to solvrow transformation methodinverse matrix method and so on.  The triangular decomposition method is also one of that.e a set of equations in matrix algebra like Gaussian elimination method, 

 

Here I’ll only work with the 3 × 3 matrices. Soon, I’ll also write on the triangular decomposition method in 4 × 4 matrices.

Here I’ll explain how to use the row transformation method to solve a set of equations.

METHOD

Suppose I have a set of equations like 

  \begin{eqnarray*} a_1x+a_2y+a_3z &=&b_1\\ a_4 x+a_5 y+a_6 z&=&b_2\\ a_7x+a_8y+a_9z &=&b_3. \end{eqnarray*}

Now I have to solve these equations using the triangular decomposition method.

Step 1

First of all, I’ll write the set of equations in a matrix form.

Thus it will be 

  \[ \begin{pmatrix} a_1&a_2&a_3\\ a_4 &a_5 &a_6\\ a_7 &a_8 &a_9 \end{pmatrix}\begin{pmatrix} x\\ y\\ z \end{pmatrix}=\begin{pmatrix} b_1\\ b_2\\ b_3 \end{pmatrix}. \]

So I can say the system of equations is in the form of \textbf{A x = b}.

Here \textbf{A} is the coefficient matrix, \textbf{x} is the variable matrix and \textbf{b} is the constant matrix.

In the method of triangular decomposition, the first task is to write the coefficient matrix \textbf{A} as a product of two matrices \textbf{L} and \textbf{U}.

The matrix \textbf{L} will be a lower triangular matrix. And, the matrix \textbf{U} will be an upper triangular matrix.

Thus it will be \textbf{A = L U}.

Here the matrix \textbf{L} will look like 

  \[ \textbf{L} = \begin{pmatrix} l_{11}&0&0\\ l_{21} &l_{22} &0\\ l_{31} &l_{32} &l_{33} \end{pmatrix}. \]

Similarly, the matrix \textbf{U} will be 

  \[ \textbf{U} = \begin{pmatrix} 1&u_{12}&u_{13}\\ 0 &1 &u_{23}\\ 0 &0 &1 \end{pmatrix}. \]

Thus the system of equations will be

  \[\textbf{A x = b} \Rightarrow \textbf{(L U) x = b}.\]

Step 2

Now I’ll choose \textbf{U x = y}.

Here the matrix \textbf{y} will be 

  \[ \textbf{y} = \begin{pmatrix} y_1\\ y_2\\ y_3 \end{pmatrix}. \]

So I can say 

  \[\textbf{A x = b} \Rightarrow \textbf{L (U x) = b} \Rightarrow \textbf{L y= b}.\]

Then I’ll solve for \textbf{y}. Next, I’ll solve for \textbf{x} using the relation  \textbf{U x = y}.

Now I’ll solve an example on that.

EXAMPLE OF THE TRIANGULAR DECOMPOSITION METHOD IN 3 × 3 MATRICES

Here is the example of the triangular decomposition method in 3 × 3 matrices.

EXAMPLE

According to Stroud and Booth (2011) “Using the method of triangular decomposition, solve the following set of equations.

  \[ \begin{pmatrix} 1&4&-1\\ 4 &2 &3\\ 7 &-3 &2 \end{pmatrix}\begin{pmatrix} x_1\\ x_2\\ x_3 \end{pmatrix}=\begin{pmatrix} -2\\ -1\\ -18 \end{pmatrix}. \]

SOLUTION

Here I know the set of equations as 

  \[ \begin{pmatrix} 1&4&-1\\ 4 &2 &3\\ 7 &-3 &2 \end{pmatrix}\begin{pmatrix} x_1\\ x_2\\ x_3 \end{pmatrix}=\begin{pmatrix} -2\\ -1\\ -18 \end{pmatrix}. \]

So I can say that the coefficient matrix \textbf{A} is 

  \[ \textbf{A} = \begin{pmatrix} 1&4&-1\\ 4 &2 &3\\ 7 &-3 &2 \end{pmatrix}. \]

Now my first job is to write the matrix \textbf{A} as a product of two matrices \textbf{L} and \textbf{U}.

So I choose the lower triangular matrix \textbf{L} as 

  \[ \textbf{L} = \begin{pmatrix} l_{11}&0&0\\ l_{21} &l_{22} &0\\ l_{31} &l_{32} &l_{33} \end{pmatrix}. \]

Next, I choose the upper triangular matrix \textbf{U} as 

  \[ \textbf{U} = \begin{pmatrix} 1&u_{12}&u_{13}\\ 0 &1 &u_{23}\\ 0 &0 &1 \end{pmatrix}. \]

Therefore it will be \textbf{A = LU}.

So that means 

  \[ \begin{pmatrix} 1&4&-1\\ 4 &2 &3\\ 7 &-3 &2 \end{pmatrix} =  \begin{pmatrix} l_{11}&0&0\\ l_{21} &l_{22} &0\\ l_{31} &l_{32} &l_{33} \end{pmatrix} \begin{pmatrix} 1&u_{12}&u_{13}\\ 0 &1 &u_{23}\\ 0 &0 &1 \end{pmatrix}. \]

Now I’ll get the matrices \textbf{L} and \textbf{U} by solving this system.

STEP 1

I’ll start with matrix multiplication.

First of all, I’ll multiply both the matrices \textbf{L} and \textbf{U} to get

  \[ \begin{pmatrix} l_{11}&l_{11}u_{12}&l_{11}u_{13}\\ l_{21} &l_{21}u_{12}+l_{22} &l_{21}u_{13}+l_{22}u_{23}\\ l_{31} &l_{31}u_{12} + l_{32} &l_{31}u_{13}+ l_{32}u_{23} + l_{33} \end{pmatrix} = \begin{pmatrix} 1&4&-1\\ 4 &2 &3\\ 7 &-3 &2 \end{pmatrix} . \]

So now I have nine equations to solve.

I’ll start with the first row.

STEP 2

The first one is the element from the first row and the first column.

Thus I can say

(1) \begin{equation*} l_{11}=1. \end{equation*}

Next, is the element from the first row and the second column.

So it will be 

  \[l_{11}u_{12}=4.\]

From equation (1), I can already say l_{11}=1.

Thus the value of u_{12} will be 

(2) \begin{equation*} u_{12}=4. \end{equation*}

Now comes the element from the first row and the third column.

So it will be 

  \[l_{11}u_{13}=-1.\]

From equation (1), I can already say l_{11}=1.

Thus the value of u_{13} will be 

(3) \begin{equation*} u_{13}=-1. \end{equation*}

Now it’s time for the second row.

STEP 3

Next, comes the element from the second row and the first column.

So it will be

(4) \begin{equation*} l_{21}=4. \end{equation*}

Now comes the element from the second row and the second column.

So it will be 

  \[l_{21}u_{12} + l_{22}=2.\]

From equations (2) and (4), I can already say l_{21}=4, u_{12} = 4.

Thus the value of l_{22} will be

  \[(4)(4) + l_{22} = 2.\]

This means

(5) \begin{equation*} l_{22}=-14. \end{equation*}

Next, comes the element from the second row and the third column.

So it will be 

  \[l_{21}u_{13} + l_{22}u_{23}=3.\]

From equations (3), (4) and (5), I can already say l_{21}=4, u_{13} = -1, l_{22} = -14.

Thus the value of u_{23} will be

  \[(4)(-1) + (-14) u_{23} = 3.\]

This means

(6) \begin{equation*} u_{23}=-\frac{1}{2}. \end{equation*}

Now comes the third row.

STEP 4

First comes the element from the third row and the first column.

So it will be

(7) \begin{equation*} l_{31}=7. \end{equation*}

Next comes the element from the third row and the second column.

So it will be 

  \[l_{31}u_{12} + l_{32} = -3.\]

From equations (2) and (7), I can already say u_{12}=4, l_{31} = 7.

Thus the value of l_{32} will be

  \[(7)(4) + l_{32} = -3.\]

This means

(8) \begin{equation*} l_{32} = -31. \end{equation*}

At the end comes the element from the third row and the third column.

So it will be 

  \[l_{31}u_{13} + l_{32}u_{23} + l_{33} = 2.\]

From equations (3), (6), (7) and (8) I can already say u_{13}= -1, u_{23} = -\cfrac{1}{2}, l_{31} = 7, l_{32} = -31.

Thus the value of l_{33} will be

  \[(7)(-1) +(-31)\left(-\frac{1}{2}\right) + l_{33} = 2.\]

This means

(9) \begin{equation*} l_{33} = -\frac{13}{2}. \end{equation*}

Now I’ll use equations  (1) – (9) to write the matrices \textbf{L} and \textbf{U}.

Thus it will be 

  \[ \textbf{L} = \begin{pmatrix} 1&0&0\\ 4 &-14&0\\ 7 &-31 &-\frac{13}{2} \end{pmatrix} \]

and 

  \[ \textbf{U} = \begin{pmatrix} 1&4&-1\\ 0 &1 &-\frac{1}{2}\\ 0 &0 &1 \end{pmatrix} .\]



STEP 5

Now I’ll choose \textbf{U x = y}.

Here the matrix \textbf{y} will be 

  \[ \textbf{y} = \begin{pmatrix} y_1\\ y_2\\ y_3 \end{pmatrix}. \]

So I can say 

  \[\textbf{A x = b} \Rightarrow \textbf{L (U x) = b} \Rightarrow \textbf{L y= b}.\]

Now I’ll solve for \textbf{y}.

Thus it will be 

  \[  \begin{pmatrix} 1&0&0\\ 4 &-14&0\\ 7 &-31 &-\frac{13}{2} \end{pmatrix}  \begin{pmatrix} y_1\\ y_2\\ y_3 \end{pmatrix} = \begin{pmatrix} -2\\ -1\\ -18 \end{pmatrix}. \]

Next I’ll do the matrix multiplication on the left-hand-side.

Therefore it will look like 

  \[  \begin{pmatrix} y_1\\ 4y_1 - 14y_2\\ 7y_1 -31y_2 - \frac{13}{2}y_ 3 \end{pmatrix}  = \begin{pmatrix} -2\\ -1\\ -18 \end{pmatrix}. \]

So I get three equations now.

First one is 

(10) \begin{equation*} y_1 = - 2. \end{equation*}

Next equation is

  \[4y_1 - 14y_2 = -1.\]

Using equation (10), I can say that 

  \[4(-2) - 14y_2 = -1.\]

This gives 

(11) \begin{equation*} y_2 = - \frac{1}{2}. \end{equation*}

The last equation is

  \[7y_1 - 31y_2 - \frac{13}{2}y_3 = -18.\]

Using equations (10) and (11), I can say that 

  \[7(-2) - 31\left(- \frac{1}{2}\right) - \frac{13}{2}y_3 = -18.\]

This gives 

(12) \begin{equation*} y_3 = 3. \end{equation*}

So I can say 

  \[  \begin{pmatrix} y_1\\ y_2\\ y_3 \end{pmatrix} = \begin{pmatrix} -2\\ - \frac{1}{2}\\ 3 \end{pmatrix}. \]

STEP 6

Now I’ll solve for \textbf{x} using the relation \textbf{U x = y}.

From Step 4, I already know that the matrix \textbf{U} is

  \[ \textbf{U} = \begin{pmatrix} 1&4&-1\\ 0 &1 &-\frac{1}{2}\\ 0 &0 &1 \end{pmatrix} .\]

Thus it will be 

  \[ \begin{pmatrix} 1&4&-1\\ 0 &1 &-\frac{1}{2}\\ 0 &0 &1 \end{pmatrix}\begin{pmatrix} x_1\\ x_2\\ x_3 \end{pmatrix} =  \begin{pmatrix} -2\\ - \frac{1}{2}\\ 3 \end{pmatrix}.\]

Again I’ll use matrix multiplication on the left-hand-side.

Therefore it will be 

  \[ \begin{pmatrix} x_1&4x_2&-x_3\\ 0 &x_2 &-\frac{1}{2}x_3\\ 0 &0 &x_3 \end{pmatrix} =  \begin{pmatrix} -2\\ - \frac{1}{2}\\ 3 \end{pmatrix}.\]

Like Step 5, here again I get three equations like

  \begin{eqnarray*} x_1 + 4x_2 - x_3 &=& - 2\\ x_2 - \frac{1}{2}x_3 &=& - \frac{1}{2}\\x_3 &=& 3. \end{eqnarray*}

From the last equation, I already know that x_3 = 3.

From the middle equation, I can say that x_2 - \frac{1}{2}(3) = - \frac{1}{2}.

This gives x_2 = 1.

The first equation gives x_1 + 4(1) - 3 = - 2.

This means x_1 = -3.

Hence I can conclude that by using the triangular decomposition method in 3 × 3 matrices I get the solution of the set of equations as 

  \[ \begin{pmatrix} x_1\\ x_2\\ x_3 \end{pmatrix} =  \begin{pmatrix} -3\\ 1\\ 3 \end{pmatrix}.\]

This is the answer to the given example.