# Program for Gauss Siedel Method (Computational Mathematics)

The Gauss Seidel method is an iterative process to solve a square system of (multiple) linear equations. It is also prominently known as ‘Liebmann’ method. In any iterative method in numerical analysis, every solution attempt is started with an approximate solution of an equation and iteration is performed until the desired accuracy is obtained. In Gauss-Seidel method, the most recent values are used in successive iterations. The Gauss-Seidel Method allows the user to control round-off error.

The Gauss Seidel method is very similar to Jacobi method and is called as the method of successive displacement. (Since recently obtained values are used in the subsquent equations). The Gauss Seidel convergence criteria depend upon the following two properties: (must be satisfied).

• The matrix is diagonally dominant.
• The matrix is symmetrical and positive.

Steps involved:

• Step 1: Compute value for all the linear equations for Xi. (Initial array must be available)
• Step 2: Compute each Xi and repeat the above steps.
• Step 3: Make use of the absolute relative approximate error after every step to check if the error occurs within a pre-specified tolerance.

Code for Gauss Siedel method:

 `#include ` ` `  `int` `main() ` `{ ` `    ``int` `count, t, limit; ` `    ``float` `temp, error, a, sum = 0; ` `    ``float` `matrix, y, allowed_error; ` `     `  `    ``printf``(``"\nEnter the Total Number of Equations:\t"``); ` `    ``scanf``(``"%d"``, & limit); ` `    ``// maximum error limit till which errors are considered,  ` `    ``// or desired accuracy is obtained) ` `     `  `    ``printf``(``"Enter Allowed Error:\t"``);  ` `    ``scanf``(``"%f"``, & allowed_error); ` `    ``printf``(``"\nEnter the Co-Efficients\n"``); ` `     `  `    ``for``(count = 1; count < = limit; count++) ` `    ``{ ` `        ``for``(t = 1; t < = limit + 1; t++) ` `        ``{ ` `            ``printf``(" Matrix[%d][%d] = " , count, t); ` `            ``scanf``(" %f" , & matrix[count][t]); ` `        ``} ` `    ``} ` `     `  `    ``for``(count = 1; count < = limit; count++) ` `    ``{ ` `        ``y[count] = 0; ` `    ``} ` `    ``do` `    ``{ ` `        ``a = 0; ` `        ``for``(count = 1; count < = limit; count++) ` `        ``{ ` `            ``sum = 0; ` `            ``for``(t = 1; t  a) ` `            ``{ ` `                ``a = error; ` `            ``} ` `            ``y[count] = temp; ` `            ``printf``(``"\nY[%d]=\t%f"``, count, y[count]); ` `        ``} ` `        ``printf``(``"\n"``); ` `    ``} ` `    ``while``(a > = allowed_error); ` `     `  `    ``printf``(``"\n\nSolution\n\n"``); ` `     `  `    ``for``(count = 1; count < = limit; count++) ` `    ``{ ` `        ``printf``(" \nY[%d]:\t%f" , count, y[count]); ` `    ``} ` `    ``return` `0; ` `} `

Output:

```Enter the Total Number of Equations:   1
Enter Allowed Error:   0.5

Enter the Co-Efficients
Matrix = 1
Matrix = 4

Y=   4.000000

Y=   4.000000

Solution

Y:   4.000000  ```

• Faster iteration process. (than other methods)
• Simple and easy to implement.
• Low on memory requirements.

• Slower rate of convergence. (than other methods)
• Requires a large number of iterations to reach the convergence point.

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