The Jacobi Iterative method question

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SUMMARY

The discussion centers on the application of the Jacobi Iterative method to solve the linear system represented by the matrix equation Ax = B, where matrix A is not diagonally dominant. The user identifies that the Jacobi method may not converge reliably due to this lack of diagonal dominance, despite the method's potential for convergence under certain conditions. The user has attempted to use an initial guess of x^(0) = (3, 0, 3, 1) but found the resulting solution set to be inaccurate. In contrast, they successfully solved the system using Gaussian Elimination with partial pivoting and LU Decomposition.

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  • Understanding of the Jacobi Iterative method
  • Knowledge of matrix properties, specifically diagonal dominance
  • Familiarity with numerical methods for solving linear systems
  • Experience with Gaussian Elimination and LU Decomposition techniques
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  • Learn about alternative iterative methods such as Gauss-Seidel and Successive Over-Relaxation (SOR)
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Students and professionals in numerical analysis, particularly those working with iterative methods for solving linear equations, as well as anyone seeking to understand the limitations of the Jacobi method in practical applications.

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Homework Statement



(Ax = B)

A:

3.1410 -2.7180 1.4140 -1.7321
9.8690 2.7180 -7.3890 0.4280
2.2360 -2.4490 1.0000 -1.4140
31.0060 7.3890 -2.6450 0.1110

B:

3.316
0
3.141
1.414

The question in my Numerical Methods assignment asks to use the Jacobi Iterative method to solve the system.

Homework Equations



The Jacobi Iterative method works ONLY IF a matrix is diagonally dominant. It's not mentioned in my Numerical Analysis textbook. I have only found out about this on wiki.org.

I have found out that the matrix A (See above) is not diagonally dominant. So the method will not work on this system (Ax = B).

Did I miss something? What should I do? I tried the method very carefully. The solution set doesn't make sense.
 
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Jacobi method can converge even if the matrix is not diagonally dominant. However, you notice immediately from the iteration formula
x_i^{n+1} = \frac{b_i - \sum_{j \neq i} a_{ij} x^{n}_j}{a_{ii}}
that if the matrix is not diagonally dominant,
\frac{\sum_{j \neq i} a_{ij}}{a_{ii}} \gt 1
then the convergence depends on the initial value you choose for x. Perhaps you can make a better guess for the initial x, or if that fails, look up the correct value from wolfram alpha and adjust your guess accordingly. :-)
 
The initial x^(0) (provided by the assignment question paper) is x^(0) = (3, 0, 3, 1).

I tried this. It converged to some solution set but the problem is that this solution set is extremely inaccurate.

I have no problem finding the right solution set when applying Gaussian Elimination (with partial pivoting) and LU Decomposition method on this system (Ax = b).

I can't say the same for the Jacobi method :(
 

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