def jacobi(A, x, b, iterations=1, omega=1.0):
    """Perform Jacobi iteration on the linear system Ax=b.

    Parameters
    ----------
    A : csr_matrix
        Sparse NxN matrix
    x : ndarray
        Approximate solution (length N)
    b : ndarray
        Right-hand side (length N)
    iterations : int
        Number of iterations to perform
    omega : scalar
        Damping parameter

    Returns
    -------
    Nothing, x will be modified in place.

    Examples
    --------
    >>> # Use Jacobi as a Stand-Alone Solver
    >>> from pyamg.relaxation.relaxation.relaxation import jacobi
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy as np
    >>> A = poisson((10,10), format='csr')
    >>> x0 = np.zeros((A.shape[0],1))
    >>> b = np.ones((A.shape[0],1))
    >>> jacobi(A, x0, b, iterations=10, omega=1.0)
    >>> print norm(b-A*x0)
    5.83475132751
    >>> #
    >>> # Use Jacobi as the Multigrid Smoother
    >>> from pyamg import smoothed_aggregation_solver
    >>> sa = smoothed_aggregation_solver(A, B=np.ones((A.shape[0],1)),
    ...         coarse_solver='pinv2', max_coarse=50,
    ...         presmoother=('jacobi', {'omega': 4.0/3.0, 'iterations' : 2}),
    ...         postsmoother=('jacobi', {'omega': 4.0/3.0, 'iterations' : 2}))
    >>> x0=np.zeros((A.shape[0],1))
    >>> residuals=[]
    >>> x = sa.solve(b, x0=x0, tol=1e-8, residuals=residuals)

    """
    A, x, b = make_system(A, x, b, formats=['csr', 'bsr'])

    sweep = slice(None)
    (row_start, row_stop, row_step) = sweep.indices(A.shape[0])

    if (row_stop - row_start) * row_step <= 0:  # no work to do
        return

    temp = np.empty_like(x)

    # Create uniform type, convert possibly complex scalars to length 1 arrays
    [omega] = type_prep(A.dtype, [omega])

    if sparse.isspmatrix_csr(A):
        for iter in range(iterations):
            amg_core.jacobi(A.indptr, A.indices, A.data, x, b, temp,
                            row_start, row_stop, row_step, omega)
    else:
        R, C = A.blocksize
        if R != C:
            raise ValueError('BSR blocks must be square')
        row_start = int(row_start / R)
        row_stop = int(row_stop / R)
        for iter in range(iterations):
            amg_core.bsr_jacobi(A.indptr, A.indices, np.ravel(A.data),
                                x, b, temp, row_start, row_stop,
                                row_step, R, omega)
Perform Jacobi iteration on the linear system Ax=b.

    Parameters
    ----------
    A : csr_matrix
        Sparse NxN matrix
    x : ndarray
        Approximate solution (length N)
    b : ndarray
        Right-hand side (length N)
    iterations : int
        Number of iterations to perform
    omega : scalar
        Damping parameter

    Returns
    -------
    Nothing, x will be modified in place.

    Examples
    --------
    >>> # Use Jacobi as a Stand-Alone Solver
    >>> from pyamg.relaxation.relaxation.relaxation import jacobi
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy as np
    >>> A = poisson((10,10), format='csr')
    >>> x0 = np.zeros((A.shape[0],1))
    >>> b = np.ones((A.shape[0],1))
    >>> jacobi(A, x0, b, iterations=10, omega=1.0)
    >>> print norm(b-A*x0)
    5.83475132751
    >>> #
    >>> # Use Jacobi as the Multigrid Smoother
    >>> from pyamg import smoothed_aggregation_solver
    >>> sa = smoothed_aggregation_solver(A, B=np.ones((A.shape[0],1)),
    ...         coarse_solver='pinv2', max_coarse=50,
    ...         presmoother=('jacobi', {'omega': 4.0/3.0, 'iterations' : 2}),
    ...         postsmoother=('jacobi', {'omega': 4.0/3.0, 'iterations' : 2}))
    >>> x0=np.zeros((A.shape[0],1))
    >>> residuals=[]
    >>> x = sa.solve(b, x0=x0, tol=1e-8, residuals=residuals)