However, the manual computation of Gauss Seidel/Jacobi method can also be lengthy. This method is fast and easy compared to the direct methods such as Gauss Jordan method, Gauss Elimination method, Cramer’s rule, etc. Programs in any high level programming language can be written with the help of these Gauss-Seidel and Gauss Jacobi method algorithm and flowchart to solve linear simultaneous equations. I was thinking that there would be some dependence of optimal on parameters c, r. If absolute value of ((x – x0) / x) > er, then I was experimenting with SOR for matrix of this king: A I + C + R where I is identity matrix, C i j c i, j and R i j s are random numbers from unifrom distribution such that R i j < r.
In this method, we should see that the variable absolute value coefficient is greater than or equal to sum of the absolute values of the coefficient of the remaining variables. To get better values, the approximations in previous iterations are used.
In Gauss Jacobi method, we assume x1, x2 and x3 as the three initial guesses. Whether it’s a program, algorithm, or flowchart, we start with a guess solution of the given system of linear simultaneous equations, and iterate the equations till the desired degree of accuracy is reached.
Both are based on fixed point iteration method. Gauss-Seidel and Gauss Jacobi method are iterative methods used to find the solution of a system of linear simultaneous equations.
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