Exemple #1
0
def FC_jacobi(A, x, b, Cpts, Fpts, iterations=1, F_iterations=1,
              C_iterations=1, omega=1.0):
    """Perform FC Jacobi iteration on the linear system Ax=b, that is

        x_f = (1-omega)x_f + omega*Dff^{-1}(b_f - Aff*xf - Afc*xc)
        x_c = (1-omega)x_c + omega*Dff^{-1}(b_c - Acf*xf - Acc*xc)

    where xf is x restricted to F-points, and likewise for c subscripts.

    Parameters
    ----------
    A : csr_matrix
        Sparse NxN matrix
    x : ndarray
        Approximate solution (length N)
    b : ndarray
        Right-hand side (length N)
    Cpts : array ints
        List of C-points
    Fpts : array ints
        List of F-points
    iterations : int
        Number of iterations to perform of total FC-cycle
    F_iterations : int
        Number of sweeps of F-relaxation to perform
    C_iterations : int
        Number of sweeps of C-relaxation to perform
    omega : scalar
        Damping parameter

    Returns
    -------
    Nothing, x will be modified in place.
    """
    A, x, b = make_system(A, x, b, formats=['csr', 'bsr'])

    # 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):
            for Fiter in range(F_iterations):
                amg_core.jacobi_indexed(A.indptr, A.indices, A.data, x, b, Fpts, omega)
            for Citer in range(C_iterations):
                amg_core.jacobi_indexed(A.indptr, A.indices, A.data, x, b, Cpts, omega)
    else:
        R, C = A.blocksize
        if R != C:
            raise ValueError('BSR blocks must be square')

        for iter in range(iterations):
            for Fiter in range(F_iterations):
                amg_core.bsr_jacobi_indexed(A.indptr, A.indices, np.ravel(A.data),
                                            x, b, Fpts, R, omega)
            for Citer in range(C_iterations):
                amg_core.bsr_jacobi_indexed(A.indptr, A.indices, np.ravel(A.data),
                                            x, b, Cpts, R, omega)
Exemple #2
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def jacobi_ne(A, x, b, iterations=1, omega=1.0):
    """Perform Jacobi iterations on the linear system A A.H x = A.H b
       (Also known as Cimmino relaxation)
    
    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.

    References
    ----------
    .. [1] Brandt, Ta'asan.  
       "Multigrid Method For Nearly Singular And Slightly Indefinite Problems."
       1985.  NASA Technical Report Numbers: ICASE-85-57; NAS 1.26:178026; NASA-CR-178026;

    .. [2] Kaczmarz.  Angenaeherte Aufloesung von Systemen Linearer Gleichungen. 
       Bull. Acad.  Polon. Sci. Lett. A 35, 355-57.  1937 

    .. [3] Cimmino. La ricerca scientifica ser. II 1. 
       Pubbliz. dell'Inst. pre le Appl. del Calculo 34, 326-333, 1938.
    
    Examples
    --------
    >>> ## Use NE Jacobi as a Stand-Alone Solver
    >>> from pyamg.relaxation import jacobi_ne
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy
    >>> A = poisson((50,50), format='csr')
    >>> x0 = numpy.zeros((A.shape[0],1))
    >>> b = numpy.ones((A.shape[0],1))
    >>> jacobi_ne(A, x0, b, iterations=10, omega=2.0/3.0)
    >>> print norm(b-A*x0)
    49.3886046066
    >>> #
    >>> ## Use NE Jacobi as the Multigrid Smoother
    >>> from pyamg import smoothed_aggregation_solver
    >>> sa = smoothed_aggregation_solver(A, B=numpy.ones((A.shape[0],1)),
    ...         coarse_solver='pinv2', max_coarse=50,
    ...         presmoother=('jacobi_ne', {'iterations' : 2, 'omega' : 4.0/3.0}), 
    ...         postsmoother=('jacobi_ne', {'iterations' : 2, 'omega' : 4.0/3.0}))
    >>> x0=numpy.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'])
    
    sweep = slice(None)
    (row_start,row_stop,row_step) = sweep.indices(A.shape[0])
    
    temp = numpy.zeros_like(x)
    
    # Dinv for A*A.H
    Dinv = get_diagonal(A, norm_eq=2, inv=True)
    
    
    # Create uniform type, and convert possibly complex scalars to length 1 arrays
    [omega] = type_prep(A.dtype, [omega])
    
    for i in range(iterations):
        delta = (numpy.ravel(b - A*x)*numpy.ravel(Dinv)).astype(A.dtype)
        amg_core.jacobi_ne(A.indptr, A.indices, A.data,
                                       x, b, delta, temp, row_start,
                                       row_stop, row_step, omega)  
Exemple #3
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def block_jacobi(A, x, b, Dinv=None, blocksize=1, iterations=1, omega=1.0):
    """Perform block Jacobi iteration on the linear system Ax=b

    Parameters
    ----------
    A : csr_matrix or bsr_matrix
        Sparse NxN matrix
    x : ndarray
        Approximate solution (length N)
    b : ndarray
        Right-hand side (length N)
    Dinv : array
        Array holding block diagonal inverses of A 
        size (N/blocksize, blocksize, blocksize)
    blocksize : int
        Desired dimension of blocks
    iterations : int
        Number of iterations to perform
    omega : scalar
        Damping parameter

    Returns
    -------
    Nothing, x will be modified in place.
   
    Examples
    --------
    >>> ## Use block Jacobi as a Stand-Alone Solver
    >>> from pyamg.relaxation import *
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy
    >>> A = poisson((10,10), format='csr')
    >>> x0 = numpy.zeros((A.shape[0],1))
    >>> b = numpy.ones((A.shape[0],1))
    >>> block_jacobi(A, x0, b, blocksize=4, iterations=10, omega=1.0)
    >>> print norm(b-A*x0)
    4.66474230129
    >>> #
    >>> ## Use block Jacobi as the Multigrid Smoother
    >>> from pyamg import smoothed_aggregation_solver
    >>> sa = smoothed_aggregation_solver(A, B=numpy.ones((A.shape[0],1)),
    ...        coarse_solver='pinv2', max_coarse=50,
    ...        presmoother=('block_jacobi', {'omega': 4.0/3.0, 'iterations' : 2, 'blocksize' : 4}), 
    ...        postsmoother=('block_jacobi', {'omega': 4.0/3.0, 'iterations' : 2, 'blocksize' : 4}))
    >>> x0=numpy.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'])
    A = A.tobsr(blocksize=(blocksize, blocksize))

    if Dinv == None:
        Dinv = get_block_diag(A, blocksize=blocksize, inv_flag=True)
    elif Dinv.shape[0] != A.shape[0]/blocksize:
        raise ValueError('Dinv and A have incompatible dimensions')
    elif (Dinv.shape[1] != blocksize) or (Dinv.shape[2] != blocksize):
        raise ValueError('Dinv and blocksize are incompatible')
    
    sweep = slice(None)
    (row_start,row_stop,row_step) = sweep.indices(A.shape[0]/blocksize)

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

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

    for iter in xrange(iterations):
        amg_core.block_jacobi(A.indptr, A.indices, numpy.ravel(A.data),
                              x, b, numpy.ravel(Dinv), temp,
                              row_start, row_stop, row_step,
                              omega, blocksize)
Exemple #4
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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 import *
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy
    >>> A = poisson((10,10), format='csr')
    >>> x0 = numpy.zeros((A.shape[0],1))
    >>> b = numpy.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=numpy.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=numpy.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 = numpy.empty_like(x)
    
    # Create uniform type, and convert possibly complex scalars to length 1 arrays
    [omega] = type_prep(A.dtype, [omega])

    if sparse.isspmatrix_csr(A):
        for iter in xrange(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 = row_start / R
        row_stop  = row_stop  / R
        for iter in xrange(iterations):
            amg_core.bsr_jacobi(A.indptr, A.indices, numpy.ravel(A.data),
                     x, b, temp, row_start, row_stop, row_step, R, omega)
Exemple #5
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def jacobi_ne(A, x, b, iterations=1, omega=1.0):
    """Perform Jacobi iterations on the linear system A A.H x = A.H b
       (Also known as Cimmino relaxation)
    
    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.

    References
    ----------
    .. [1] Brandt, Ta'asan.  
       "Multigrid Method For Nearly Singular And Slightly Indefinite Problems."
       1985.  NASA Technical Report Numbers: ICASE-85-57; NAS 1.26:178026; NASA-CR-178026;

    .. [2] Kaczmarz.  Angenaeherte Aufloesung von Systemen Linearer Gleichungen. 
       Bull. Acad.  Polon. Sci. Lett. A 35, 355-57.  1937 

    .. [3] Cimmino. La ricerca scientifica ser. II 1. 
       Pubbliz. dell'Inst. pre le Appl. del Calculo 34, 326-333, 1938.
    
    Examples
    --------
    >>> ## Use NE Jacobi as a Stand-Alone Solver
    >>> from pyamg.relaxation import jacobi_ne
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy
    >>> A = poisson((50,50), format='csr')
    >>> x0 = numpy.zeros((A.shape[0],1))
    >>> b = numpy.ones((A.shape[0],1))
    >>> jacobi_ne(A, x0, b, iterations=10, omega=2.0/3.0)
    >>> print norm(b-A*x0)
    49.3886046066
    >>> #
    >>> ## Use NE Jacobi as the Multigrid Smoother
    >>> from pyamg import smoothed_aggregation_solver
    >>> sa = smoothed_aggregation_solver(A, B=numpy.ones((A.shape[0],1)),
    ...         coarse_solver='pinv2', max_coarse=50,
    ...         presmoother=('jacobi_ne', {'iterations' : 2, 'omega' : 4.0/3.0}), 
    ...         postsmoother=('jacobi_ne', {'iterations' : 2, 'omega' : 4.0/3.0}))
    >>> x0=numpy.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'])

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

    temp = numpy.zeros_like(x)

    # Dinv for A*A.H
    Dinv = get_diagonal(A, norm_eq=2, inv=True)

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

    for i in range(iterations):
        delta = (numpy.ravel(b - A * x) * numpy.ravel(Dinv)).astype(A.dtype)
        amg_core.jacobi_ne(A.indptr, A.indices, A.data, x, b, delta, temp,
                           row_start, row_stop, row_step, omega)
Exemple #6
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def block_jacobi(A, x, b, Dinv=None, blocksize=1, iterations=1, omega=1.0):
    """Perform block Jacobi iteration on the linear system Ax=b

    Parameters
    ----------
    A : csr_matrix or bsr_matrix
        Sparse NxN matrix
    x : ndarray
        Approximate solution (length N)
    b : ndarray
        Right-hand side (length N)
    Dinv : array
        Array holding block diagonal inverses of A 
        size (N/blocksize, blocksize, blocksize)
    blocksize : int
        Desired dimension of blocks
    iterations : int
        Number of iterations to perform
    omega : scalar
        Damping parameter

    Returns
    -------
    Nothing, x will be modified in place.
   
    Examples
    --------
    >>> ## Use block Jacobi as a Stand-Alone Solver
    >>> from pyamg.relaxation import *
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy
    >>> A = poisson((10,10), format='csr')
    >>> x0 = numpy.zeros((A.shape[0],1))
    >>> b = numpy.ones((A.shape[0],1))
    >>> block_jacobi(A, x0, b, blocksize=4, iterations=10, omega=1.0)
    >>> print norm(b-A*x0)
    4.66474230129
    >>> #
    >>> ## Use block Jacobi as the Multigrid Smoother
    >>> from pyamg import smoothed_aggregation_solver
    >>> sa = smoothed_aggregation_solver(A, B=numpy.ones((A.shape[0],1)),
    ...        coarse_solver='pinv2', max_coarse=50,
    ...        presmoother=('block_jacobi', {'omega': 4.0/3.0, 'iterations' : 2, 'blocksize' : 4}), 
    ...        postsmoother=('block_jacobi', {'omega': 4.0/3.0, 'iterations' : 2, 'blocksize' : 4}))
    >>> x0=numpy.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'])
    A = A.tobsr(blocksize=(blocksize, blocksize))

    if Dinv == None:
        Dinv = get_block_diag(A, blocksize=blocksize, inv_flag=True)
    elif Dinv.shape[0] != A.shape[0] / blocksize:
        raise ValueError('Dinv and A have incompatible dimensions')
    elif (Dinv.shape[1] != blocksize) or (Dinv.shape[2] != blocksize):
        raise ValueError('Dinv and blocksize are incompatible')

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

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

    temp = numpy.empty_like(x)

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

    for iter in xrange(iterations):
        amg_core.block_jacobi(A.indptr, A.indices, numpy.ravel(A.data), x, b,
                              numpy.ravel(Dinv), temp, row_start, row_stop,
                              row_step, omega, blocksize)
Exemple #7
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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 import *
    >>> from pyamg.gallery import poisson
    >>> from pyamg.util.linalg import norm
    >>> import numpy
    >>> A = poisson((10,10), format='csr')
    >>> x0 = numpy.zeros((A.shape[0],1))
    >>> b = numpy.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=numpy.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=numpy.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 = numpy.empty_like(x)

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

    if sparse.isspmatrix_csr(A):
        for iter in xrange(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 = row_start / R
        row_stop = row_stop / R
        for iter in xrange(iterations):
            amg_core.bsr_jacobi(A.indptr, A.indices, numpy.ravel(A.data), x, b,
                                temp, row_start, row_stop, row_step, R, omega)
Exemple #8
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def FC_block_jacobi(A, x, b, Cpts, Fpts, Dinv=None, blocksize=1, iterations=1,
                    F_iterations=1, C_iterations=1, omega=1.0):
    """Perform FC block Jacobi iteration on the linear system Ax=b, that is

        x_f = (1-omega)x_f + omega*Dff^{-1}(b_f - Aff*xf - Afc*xc)
        x_c = (1-omega)x_c + omega*Dff^{-1}(b_c - Acf*xf - Acc*xc)

    where xf is x restricted to F-blocks, and Dff^{-1} the block inverse
    of the block diagonal Dff, and likewise for c subscripts.

    Parameters
    ----------
    A : csr_matrix or bsr_matrix
        Sparse NxN matrix
    x : ndarray
        Approximate solution (length N)
    b : ndarray
        Right-hand side (length N)
    Cpts : array ints
        List of C-blocks in A
    Fpts : array ints
        List of F-blocks in A
    Dinv : array
        Array holding block diagonal inverses of A
        size (N/blocksize, blocksize, blocksize)
    blocksize : int
        Desired dimension of blocks
    iterations : int
        Number of iterations to perform of total FC-cycle
    F_iterations : int
        Number of sweeps of F-relaxation to perform
    C_iterations : int
        Number of sweeps of C-relaxation to perform
    omega : scalar
        Damping parameter

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

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

    if Dinv is None:
        Dinv = get_block_diag(A, blocksize=blocksize, inv_flag=True)
    elif Dinv.shape[0] != int(A.shape[0]/blocksize):
        raise ValueError('Dinv and A have incompatible dimensions')
    elif (Dinv.shape[1] != blocksize) or (Dinv.shape[2] != blocksize):
        raise ValueError('Dinv and blocksize are incompatible')

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

    # Perform block C-relaxation then block F-relaxation
    for iter in range(iterations):
        for Fiter in range(F_iterations):
            amg_core.block_jacobi_indexed(A.indptr, A.indices, np.ravel(A.data),
                                          x, b, np.ravel(Dinv), Fpts, omega,
                                          blocksize)
        for Citer in range(C_iterations):
            amg_core.block_jacobi_indexed(A.indptr, A.indices, np.ravel(A.data),
                                          x, b, np.ravel(Dinv), Cpts, omega,
                                          blocksize)