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Jacobi method

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We will study an iterative method for solving linear systems: the Jacobi method. The aim is to build a sequence of approximations that converges to the true solution.

Iterative method

Jacobi method is an iterative method for solving linear systems such as


For this, we use a sequence x^{(k)} which converges to the fixed point(solution) x.
For x^{(0)} given, we build a sequence x^{(k)}such x^{(k+1)}=F(x^{(k)}) with k \in \mathbf{N}.

A=M-N where M is an invertible matrix.

Ax=b \Leftrightarrow 
Mx=Nx+b \Leftrightarrow & x &=& M^{-1}Nx+M^{-1}b \\
& &=& F(x)

where F is an affine function.


x^{(0)}   \textrm{ given}& ,\\
x^{(k+1)} = M^{-1}Nx^{(k)}+M^{-1}b& \textrm{else}.

If x is solution of Ax=b then x = M^{-1}Nx+M^{-1}b


Let e^{(k)} be the error vector

We put B = M^{-1}N, which gives



The algorithm converges if \lim_{k \to \infty} \| e^{(k)} \| = 0 \Leftrightarrow \lim_{k \to \infty} \| B^k \| = 0 (null matrix).

Theorem: \lim_{k \to \infty} \| B^k \| = 0 if and only if the spectral radius of the matrix
B checks:


we remind that \rho(B) = \max_{i =
1,\ldots,n} |\lambda_i| where  \lambda_1,\ldots,\lambda_n represent the eigenvalues of B.

Theorem: If A is strictly diagonally dominant,

\left | a_{ii} \right | > \sum_{i \ne j} {\left | a_{ij} \right |},\forall i=1,\ldots,n

then for all x_0 the Jacobi algorithm will converge to the solution x of the system Ax=b.

Jacobi Method

We decompose A in the following way :


- D the diagonal
- -E the strictly lower triangular part of A
- -F the strictly upper triangular part of A.

In the Jacobi’s method, we choose M = D and N = E+F (in the Gauss-Seidel Method, M = D-E and N = F).

x^{(k+1)}=D^{-1}(E+F) x^{(k)}+D^{-1}b

The i-th line of D^{-1}(E+F) is : -(\frac{a_{i,1}}{a_{i,i}},\cdots, \frac{a_{i,i-1}}{a_{i,i}},0,\frac{a_{i,i+1}}{a_{i,i}},\cdots, \frac{a_{i,n}}{a_{i,i}})

We obtain :

x^{(k+1)}_i=  -\frac{1}{a_{ii}}  \sum_{j=1,j \ne i}^n a_{ij}x^{(k)}_j + \frac{b_i}{a_{ii}}

Residual vector

Let r^{(k)}=b-Ax^{(k)} be the residual vector. We can write x_i^{(k+1)}=\frac{r_i^{(k)}}{a_{ii}} + x_i^{(k)} with r_i^{(k)} calculated
like follows

r_i^{(k+1)}=-\sum_{j=1,j \ne i}^n a_{ij} \frac{r_i^{(k)}}{a_{jj}}

Stop criteria

For the stop criteria , we can use the residual vector, wich gives for a given precision \epsilon :

\frac{\|r^{(k)} \|}{\|b\|}=\frac{\|b-Ax^{(k)} \|}{\|b\|} < \epsilon

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