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

Visual Cue	Narration
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: Solve system of linear equations using Scilab Develop Scilab code to solve linear equations
Slide 3-System Requirement slide	To record this tutorial, I am using Ubuntu 12.04 as the operating system with Scilab 5.3.3 version
Slide 4- Prerequisites slide	* To practise this tutorial, a learner should have basic knowledge of Scilab and should know how to solve Linear Equations. To learn Scilab, please refer to the relevant tutorials available on the Spoken Tutorial website.
Slide 5- System of Linear Equations	A system of linear equations is a Finite collection of linear equations of the same set of variables
Slide 6- System of Linear Equations	A system of linear equations can have one of the following conclusions No solution Unique solution Infinitely many solutions
Slide 7- Gaussian Elimination Method	Given a system of equations Ax=b (a x equal to b) with m equations and n unknowns
Slide 8- Gaussian Elimination Method	* We write the coefficients of the variables a one to a n along with the constants b one to b n of the system of the equations in one matrix called the augmented matrix
Slide 9- Gaussian Elimination Method	* How do we convert the augmented matrix to an upper triangular form matrix? We do so by performing row wise manipulation of the matrix.
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. This defines how many digits should be displayed in the answer.
Point to e	* The letter e within single quotes denotes that the answer should be displayed in scientific notation
Point to twenty	* The number 20 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 Poin to “ “	* It will display a message to the user and get the values of A and B matrices. The message should be placed within double quotes.
Extra information	* The matrices that the user inputs, 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 naivegaussianelimination.
Highlight A Highlight B	* And we state that A and B are the arguments of the function naivegaussianelimination.
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 2-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.
Highlight if n ~= n1 error('gaussianelimination - Matrix A must be square');	* Then we have to determine if the matrices are compatible with each other and if A is a square matrix. If n and n one are not equal , then we display a message that Matrix A must be square.
Highlight 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.
Highlight C=[A b]	* If the matrices are compatible, we place matrices A and b in one matrix C. This matrix C is called augmented matrix.
Highlight 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.
Highlight 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	* Finally we perform back substitution. Once the upper triangular matrix is obtained, we take the last row and find the value of the variable in that row. Then once one variable is solved, we take this variable to solve the other variables. Thus the system of linear equations is solved.
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. So we enter the values of matrix A.
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. So we type open square bracket four point seven two semi colon three point one semi colon two point nine two 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 Press enter
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 The first step is to form the augmented matrix. To do this place the coefficient matrix A and the right hand side matrix b together in one matrix. Then we perform row operations to convert matrix A to diagonal form. In diagonal form, only the elements a i i are non-zero. Rest of the elements are zero.
Slide 12- Gauss-Jordan Method	* Then we divide the diagonal element and corresponding element of right hand side element, by the diagonal element. We do this to get diagonal element equal to one. The resulting value of the elements of each row of the right hand side matrix gives the value of each variable.
Slide 13- Example	* 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.
Highlight format	* The first line of the code uses format function to specify the format of the displayed answers.
Highlight 'e'	* The parameter e specifies the answer should be in scientific notation.
Highlight 20	* 20 denotes that only 20 digits should be displayed.
Highlight 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.
Highlight function [x] = GaussJordanElimination( A, b )	* We define the function Gauss Jordan Elimination with input arguments A and b and output argument x.
Highlight [m, n] = size( A )	* We get the size of matrix A and store it in m and n
Highlight [r, s] = size( b )	* Similarly we get the size of matrix b and store it in r and s
Highlight 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.
Highlight for k = 1 : 1 : m indices = [ 1 : 1 : k-1, k+1 : 1 : m ] // For all rows below and above the pivot, subtract a multiple // 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. Here pivot refers to the first non-zero element of a column.
Highlight x = zeros( m, s )	* Then we create a matrix of zeros called x with m rows and s columns.
Highlight 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, we divide the right hand side part of augmented matrix by the corresponding diagonal element to get the value of each variable.
Highlight x(i, j) = C(i, m+j) / C(i, i)	* We store the value of each variable in x.
Highlight return x	* Then we return the value of x.
Highlight 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]	* The prompt requires us to enter the value of matrix A. So we type 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. So we type 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 Please note that the letters G J E are in capital.
Show answers on Scilab console	* The values of x one and x two are twenty and one respectively.
Slide 14- Summary	<Pause> Let us summarize. In this tutorial, we have learnt to: Develop Scilab code for solving system of linear equations Find the value of the unknown variables of a system of linear equations
Show Slide 16 Title: About the Spoken Tutorial Project Watch the video available at http://spoken-tutorial.org/What_is_a_Spoken_Tutorial It summarises the Spoken Tutorial project If you do not have good bandwidth, you can download and watch it	* About the Spoken Tutorial Project Watch the video available at http://spoken-tutorial.org/What_is_a_Spoken_Tutorial It summarises the Spoken Tutorial project If you do not have good bandwidth, you can download and watch it
Show Slide 17 Title: Spoken Tutorial Workshops The Spoken Tutorial Project Team Conducts workshops using spoken tutorials Gives certificates for those who pass an online test For more details, please write to contact@spoken-tutorial.org	The Spoken Tutorial Project Team Conducts workshops using spoken tutorials Gives certificates for those who pass an online test For more details, please write to contact at spoken hyphen tutorial dot org
Show Slide 18 Title: Acknowledgement Spoken Tutorial Project is a part of the Talk to a Teacher project It is supported by the National Mission on Education through ICT, MHRD, Government of India More information on this Mission is available at http://spoken-tutorial.org/NMEICT-Intro	* Spoken Tutorial Project is a part of the Talk to a Teacher project It is supported by the National Mission on Education through ICT, MHRD, Government of India More information on this Mission is available at spoken hyphen tutorial dot org slash NMEICT hyphen Intro
	This is Bella Tony from the FOSSEE project, IIT Bombay signing off. Thanks for joining.

Contributors and Content Editors

Lavitha Pereira, Nancyvarkey, Pratik kamble