Jacobi method and Gauss-Seidel method ,

In summary, the Jacobi method and Gauss-Seidel method are two iterative algorithms used to solve systems of linear equations. They both involve breaking down the original system and updating it until it converges to a solution. The Jacobi method is relatively easy to implement and guarantees convergence for diagonally dominant systems while the Gauss-Seidel method typically converges faster and can handle non-diagonally dominant systems. However, both methods have limitations such as the need for a starting guess and the possibility of not converging for certain systems.
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sigh1342
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Homework Statement



for part c , it asked for showing both 2 method converge for any initial condition.
I think we can show that by using $$ρ(T_{j}), ρ(T_{g}) <1 $$
I want to know whether it's correct or not , and is there any faster method?

Homework Equations


$$ρ(A)$$ means spectral radius of matrix A.
And $$ρ(T_{j})=D^{-1}(L+U) , ρ(T_{g})=(D+L)^{-1}U $$
where$$ A=D-L-U$$ , D is the diagonal matrix -L is strictly lower-triangular part of A,
-U is strictly upper-triangular part of A.

The Attempt at a Solution

 

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anyone can help ._. ?
 

What is the Jacobi method?

The Jacobi method is an iterative algorithm used to solve systems of linear equations. It involves breaking down the original system into smaller systems that can be solved individually, and then using the solutions from each smaller system to update the original system until it converges to a solution.

What is the Gauss-Seidel method?

The Gauss-Seidel method is another iterative algorithm used to solve systems of linear equations. It is similar to the Jacobi method, but it updates the original system using the most recent solutions as they become available, rather than waiting for all solutions to be calculated before updating.

What are the advantages of using the Jacobi method?

The Jacobi method is relatively easy to implement and can handle a wide range of systems. It also guarantees convergence if the system is diagonally dominant (meaning the absolute value of the diagonal element is greater than the sum of the absolute values of the other elements in the row).

What are the advantages of using the Gauss-Seidel method?

The Gauss-Seidel method typically converges faster than the Jacobi method, especially for large systems. It also does not require the system to be diagonally dominant, making it more versatile in its applications.

What are the limitations of using iterative methods like Jacobi and Gauss-Seidel?

Iterative methods may not converge to a solution for certain systems, especially if the system is not diagonally dominant. They also require a starting guess for the solution, which may affect the speed and accuracy of the convergence. In addition, they may be computationally intensive for very large systems.

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