Jacobi method convergence for hpd matrices

In summary, the conversation discusses the relationship between a squared, hermitian positive definite matrix A and the diagonal matrix D composed of its diagonal elements. It is stated that if the Jacobi iterative method converges for A, then 2D - A must also be hermitian positive definite. The conversation also mentions the iteration matrix for the Jacobi method and the condition for convergence. The person asking for help has tried several approaches but is struggling to prove the initial statement. They are looking for hints or help to solve the problem.
  • #1
AmitCarmeli
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Homework Statement


Let A be a squared, hermitian positive definite matrix. Let D denote the diagonal matrix composed of the diagonal elements of A, i.e. D = diag((A)11,(A)22,...(A)nn).

Prove that if the Jacobi iterative method converges for A, then 2D - A must also be hermitian positive definite.

Homework Equations



The Jacobi iterative method has the iteration matrix J = inv(D) * (E + E*), where A = D - E - E* is the standard decomposition of the matrix to a diagonal matrix, upper triangular matrix and lower triangular matrix.

Also, an iterative method converges if and only if the iteration matrix has a spectral radius lower than 1, i.e. r(J) < 1.

The Attempt at a Solution



I have tried several approaches. I was able to prove the inverse claim, i.e. that if 2D - A is hpd, then the Jacobi method must converge. Also, I observed that the iteration matrix for the Jacobi method of 2D - A is just -J, so the method converges for both A and 2D - A.
 
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  • #2
However, I am struggling to prove the initial statement. Could someone provide me with some hints or help?
 

1. What is the Jacobi method for solving systems of linear equations?

The Jacobi method is an iterative algorithm used to solve systems of linear equations. It involves breaking down a large matrix into smaller parts and repeatedly using these parts to update the solution until a desired level of accuracy is reached.

2. What does convergence mean in the context of the Jacobi method?

Convergence refers to the process of the Jacobi method reaching a stable and accurate solution. In other words, the algorithm has reached a point where further iterations will not significantly improve the solution.

3. How does the Jacobi method handle matrices that are not diagonally dominant?

The Jacobi method may not converge for matrices that are not diagonally dominant. In these cases, the algorithm may produce a solution that is not accurate. To ensure convergence, the matrix can be manipulated to make it diagonally dominant before applying the Jacobi method.

4. What is the role of HPD (Hermitian positive definite) matrices in the Jacobi method?

HPD matrices are a special type of matrix that have unique properties that make them suitable for use with the Jacobi method. These matrices have a symmetric structure and all their eigenvalues are positive, which ensures the convergence of the Jacobi method.

5. Can the Jacobi method be used for large matrices?

Yes, the Jacobi method can be used for large matrices. However, the convergence of the algorithm may be slower for larger matrices, and the number of iterations needed to reach a solution may be higher. Other methods such as Gauss-Seidel or SOR may be more efficient for solving large matrices.

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