# Gauss–Seidel method

Then the decomposition of A into its lower triangular component and its strictly upper triangular component is given by:

The procedure is generally continued until the changes made by an iteration are below some tolerance, such as a sufficiently small residual.

The element-wise formula for the Gauss–Seidel method is extremely similar to that of the Jacobi method.

The convergence properties of the Gauss–Seidel method are dependent on the matrix A. Namely, the procedure is known to converge if either:

The Gauss–Seidel method sometimes converges even if these conditions are not satisfied.

Since elements can be overwritten as they are computed in this algorithm, only one storage vector is needed, and vector indexing is omitted. The algorithm goes as follows:

In fact, the matrix A is strictly diagonally dominant (but not positive definite).

If we test for convergence we'll find that the algorithm diverges. In fact, the matrix A is neither diagonally dominant nor positive definite. Then, convergence to the exact solution

Suppose we choose (0, 0, 0, 0) as the initial approximation, then the first approximate solution is given by

Using the approximations obtained, the iterative procedure is repeated until the desired accuracy has been reached. The following are the approximated solutions after four iterations.

The following numerical procedure simply iterates to produce the solution vector.

This article incorporates text from the article on that is under the GFDL license.