Scilab/C4/Linear-equations-Gaussian-Methods/English
Title of script: Solving System of Linear Equations using Gauss Elimination and Gauss-Jordan Methods
Author: Shamika
Keywords: System of linear equations, Gaussian Methods
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Slide 1 | Dear Friends,
Welcome to the Spoken Tutorial on “Solving System of Linear Equations using Gauss Elimination and Gauss-Jordan Methods” |
Slide 2 -Learning Objective Slide | At the end of this tutorial, you will learn how to:
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Slide 3-System Requirement slide | To record this tutorial, I am using
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Slide 4- Prerequisites slide | To practise this tutorial, a learner
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Slide 5- System of Linear Equations | A system of linear equations is a
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Slide 7- Gaussian Elimination Method | Let us study Gauss elimination method
Given a system of equations
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Slide 8- Gaussian Elimination Method |
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Slide 9- Gaussian Elimination Method
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How do we convert the augmented matrix to an upper triangular form matrix?
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Slide 10- Example | Let us solve this system of equations using Gaussian elimination method |
Switch to Scilab and open naivegaussianelimination.sci | Before we solve the system, let us go through the code for Gaussian elimination method. |
Highlight format e comma twenty | The first line of the code is format e comma twenty.
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Point to e | The letter 'e' within single quotes denotes that the answer should be displayed in scientific notation |
Point to twenty | The number twenty is the number of digits that should be displayed. |
Highlight funcprot | The command funcprot is used to let Scilab know what to do when variables are redefined. |
Point to zero | The argument zero specifies that Scilab need not do anything if the variables are redefined. |
No visual clue. This is extra information that is being offered to the listener. | Other arguments are used to issue warnings or errors if the variables are redefined. |
Highlight input | Next we use the input function. |
Highlight input
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It will display a message to the user and get the values of A and B matrices.
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The matrices that the user enters, will be stored in the variables A and b. | |
Highlight A
Highlight B |
Here A is the coefficient matrix and b is the right-hand-side matrix or the constants matrix. |
Highlight naivegaussianelimination | Then we define the function naive gaussian elimination. |
Highlight A
Highlight b |
And we state that A and b are the arguments of the function naive gaussian elimination. |
Highlight x | We store the output in variable x. |
Highlight size | Then we find the size of matrices A and b using the size command. |
Highlight n and n one | Since they are two dimensional matrices, we use n and n one to store the size of matrix A. |
Highlight m one and p | Similarly we can use m one and p for matrix b. |
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if n ~= n1 error('gaussianelimination - Matrix A must be square'); |
Then we have to determine
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elseif n ~= m1 error('gaussianelimination - incompatible dimension of A & b'); |
If n and m one are not equal, we display a message
incompatible dimension of A and b. |
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C=[A b]; |
If the matrices are compatible, we place matrices A and b in one matrix, C.
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n=size(A,1); for k=1:n-1 for i=k+1:n factor=A(i,k)/A(k,k); for j=k+1:n A(i,j)=A(i,j)-factor*A(k,j); end b(i)=b(i)-factor*b(k); end end |
The next block of code performs forward elimination.
This code converts the augmented matrix to upper triangular matrix form.
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x(n)=b(n)/A(n,n); for i=n-1:-1:1 sum=0; for j=i+1:n sum=sum+A(i,j)*x(j); end x(i)=(b(i)-sum)/A(i,i); end |
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Click on Execute and select Save and Execute | Let us save and execute the file. |
Show Scilab Console | Switch to Scilab console to solve the example. |
Show Scilab Console | On the console, we have a prompt to enter the value of the coefficient matrix.
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Type
[3.41 1.23 -1.09;2.71 2.14 1.29;1.89 -1.91 -1.89] |
Type square bracket three point four one space one point two three space minus one point zero nine semi colon
two point seven one space two point one four space one point two nine semi colon one point eight nine space minus one point nine one space minus one point eight nine close square bracket. Press Enter |
Type
[4.72;3.1;2.92] |
The next prompt is for matrix b.
open square bracket four point seven two semi colon three point one semi colon two point nine one close square bracket. Press Enter |
Type
naivegaussianelimination(A,b) |
Then we call the function by typing
naive gaussian elimination open paranthesis A comma b close paranthesis
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Show answer on Scilab console | The solution to the system of linear equations is shown on Scilab console. |
Next we shall study the Gauss- Jordan method. | |
Slide 11 – Gauss- Jordan Method | In Gauss – Jordan Method
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Slide 12- Gauss-Jordan Method | Then we divide the diagonal element and corresponding element of right hand side element, by the diagonal element.
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Slide 13- Example
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Let us solve this example using Gauss- Jordan Method. |
Switch to Scilab console and open GaussJordan Elimination.sci | Let us look at the code first. |
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format |
The first line of the code uses format function to specify the format of the displayed answers. |
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'e' |
The parameter e specifies the answer should be in scientific notation. |
Highlight 20 | Twenty (20) denotes that only twenty digits should be displayed. |
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A=input("Enter the coeffiecient matrix : ") b=input("Enter the right-hand side matrix : ") |
Then we get the A and b matrix using the input function. |
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function [x] = GaussJordanElimination( A, b ) |
We define the function Gauss Jordan Elimination with input arguments A and b and output argument x. |
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[m, n] = size( A ) |
We get the size of matrix A and store it in m and n |
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[r, s] = size( b ) |
Similarly, we get the size of matrix b and store it in r and s |
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if ( m <> r ) then error("Error: matrix A and vector b are incompatible sizes") end |
If the sizes of A and b are not compatible, we display an error on the console using error function. |
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for k = 1 : 1 : m indices = [ 1 : 1 : k-1, k+1 : 1 : m ]
// of the pivoting row to get a zero for i = indices multiplier = C(i, k) / C(k,k) for j = k+1 : n C(i, j) = C(i, j) - multiplier * C(k, j) end end end |
Then we perform row operations to get diagonal form of the matrix.
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x = zeros( m, s ) |
Then we create a matrix of zeros called x with m rows and s columns. |
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for i = 1 : 1 : m for j = 1 : 1 : s x(i, j) = C(i, m+j) / C(i, i) end end |
Once we have the diagonal form,
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x(i, j) = C(i, m+j) / C(i, i) |
We store the value of each variable in x. |
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return x |
Then we return the value of x.
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endfunction |
Finally, we end the function. |
Click on Execute and select Save and Execute | Now let us save and execute the function. |
Type the following
[0.7, 1725;0.4352,-5.433]
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The prompt requires us to enter the value of matrix A.
open square bracket zero point seven comma one seven two five semi colon zero point four three five two comma minus five point four three three close square bracket. Press Enter |
Type the following
[1739;3.271] |
The next prompt is for vector b.
open squre bracket one seven three nine semi colon three point two seven one close square bracket Press Enter |
Type the following
GaussJordanElimination(A,b) |
Then we call the function by typing
Gauss Jordan Elimination open paranthesis A comma b close paranthesis Press Enter |
Show answers on Scilab console | The values of x one and x two are shown on the console. |
Slide 14- Summary | Let us summarize this tutorial.
In this tutorial, we have learnt to:
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Show Slide 16
Title: About the Spoken Tutorial Project
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* Watch the video available at the following link
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Show Slide 17
Title: Spoken Tutorial Workshops The Spoken Tutorial Project Team
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The Spoken Tutorial Project Team
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Show Slide 18
Title: Acknowledgement
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* Spoken Tutorial Project is a part of the Talk to a Teacher project
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This is mandatory. This is Ashwini Patil signing off. Thanks for joining. |