The Jacobi Iterative method question

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The Jacobi Iterative method requires a diagonally dominant matrix for guaranteed convergence, which the provided matrix A does not satisfy. Despite this, the method can still converge under certain conditions, depending on the initial guess for the solution vector x. The user attempted the method using the initial guess of x^(0) = (3, 0, 3, 1), but found the resulting solution set to be inaccurate. In contrast, they successfully obtained the correct solution using Gaussian Elimination and LU Decomposition methods. The discussion highlights the challenges of using the Jacobi method with non-dominant matrices and the importance of selecting a suitable initial guess.
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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 :(
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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