fipy.solvers.pyamgx package

Submodules

fipy.solvers.pyamgx.aggregationAMGSolver module

class fipy.solvers.pyamgx.aggregationAMGSolver.AggregationAMGSolver(tolerance=1e-10, iterations=2000, precon=None, smoother={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The AggregationAMGSolver is an interface to the aggregation AMG solver in AMGX, with a Jacobi smoother by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__init__(tolerance=1e-10, iterations=2000, precon=None, smoother={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.aggregationAMGSolver'

fipy.solvers.pyamgx.classicalAMGSolver module

class fipy.solvers.pyamgx.classicalAMGSolver.ClassicalAMGSolver(tolerance=1e-10, iterations=2000, precon=None, smoother={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The ClassicalAMGSolver is an interface to the classical AMG solver in AMGX, with a Jacobi smoother by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • smoother (Smoother, optional) –

  • **kwargs – Other AMGX solver options

__init__(tolerance=1e-10, iterations=2000, precon=None, smoother={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • smoother (Smoother, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.classicalAMGSolver'

fipy.solvers.pyamgx.linearBiCGStabSolver module

class fipy.solvers.pyamgx.linearBiCGStabSolver.LinearBiCGStabSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearBiCGStabSolver is an interface to the PBICGSTAB solver in AMGX, with a Jacobi preconditioner by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearBiCGStabSolver'

fipy.solvers.pyamgx.linearCGSolver module

class fipy.solvers.pyamgx.linearCGSolver.LinearCGSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearCGSolver is an interface to the PCG solver in AMGX, with no preconditioning by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearCGSolver'
fipy.solvers.pyamgx.linearCGSolver.LinearPCGSolver

alias of fipy.solvers.pyamgx.linearCGSolver.LinearCGSolver

fipy.solvers.pyamgx.linearFGMRESSolver module

class fipy.solvers.pyamgx.linearFGMRESSolver.LinearFGMRESSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearFGMRESSolver is an interface to the FGMRES solver in AMGX, with a Jacobi preconditioner by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearFGMRESSolver'

fipy.solvers.pyamgx.linearGMRESSolver module

class fipy.solvers.pyamgx.linearGMRESSolver.LinearGMRESSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearGMRESSolver is an interface to the GMRES solver in AMGX, with a Jacobi preconditioner by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearGMRESSolver'

fipy.solvers.pyamgx.linearLUSolver module

class fipy.solvers.pyamgx.linearLUSolver.LinearLUSolver(tolerance=1e-10, iterations=1000, precon=None)

Bases: fipy.solvers.scipy.scipySolver._ScipySolver

The LinearLUSolver solves a linear system of equations using LU-factorization. The LinearLUSolver is a wrapper class for the the Scipy scipy.sparse.linalg.splu module.

Create a Solver object.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon – Preconditioner to use. Not all solver suites support preconditioners.

__annotations__ = {}
__module__ = 'fipy.solvers.scipy.linearLUSolver'

fipy.solvers.pyamgx.pyAMGXSolver module

class fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver(config_dict, tolerance=1e-10, iterations=2000, precon=None, smoother=None, **kwargs)

Bases: fipy.solvers.solver.Solver

Parameters

  • config_dict (dict) – AMGX configuration options

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • smoother (Smoother, optional) –

  • **kwargs – Other AMGX solver options

__exit__(*args)
__init__(config_dict, tolerance=1e-10, iterations=2000, precon=None, smoother=None, **kwargs)
Parameters

  • config_dict (dict) – AMGX configuration options

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • smoother (Smoother, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.pyAMGXSolver'

Module contents

class fipy.solvers.pyamgx.AggregationAMGSolver(tolerance=1e-10, iterations=2000, precon=None, smoother={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The AggregationAMGSolver is an interface to the aggregation AMG solver in AMGX, with a Jacobi smoother by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__annotations__ = {}
__init__(tolerance=1e-10, iterations=2000, precon=None, smoother={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.aggregationAMGSolver'
fipy.solvers.pyamgx.DefaultAsymmetricSolver

alias of fipy.solvers.scipy.linearLUSolver.LinearLUSolver

fipy.solvers.pyamgx.DefaultSolver

alias of fipy.solvers.pyamgx.linearCGSolver.LinearCGSolver

fipy.solvers.pyamgx.DummySolver

alias of fipy.solvers.pyamgx.linearCGSolver.LinearCGSolver

fipy.solvers.pyamgx.GeneralSolver

alias of fipy.solvers.scipy.linearLUSolver.LinearLUSolver

class fipy.solvers.pyamgx.LinearBiCGStabSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearBiCGStabSolver is an interface to the PBICGSTAB solver in AMGX, with a Jacobi preconditioner by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__annotations__ = {}
__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearBiCGStabSolver'
class fipy.solvers.pyamgx.LinearCGSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearCGSolver is an interface to the PCG solver in AMGX, with no preconditioning by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__annotations__ = {}
__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearCGSolver'
class fipy.solvers.pyamgx.LinearFGMRESSolver(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)

Bases: fipy.solvers.pyamgx.pyAMGXSolver.PyAMGXSolver

The LinearFGMRESSolver is an interface to the FGMRES solver in AMGX, with a Jacobi preconditioner by default.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__annotations__ = {}
__init__(tolerance=1e-10, iterations=2000, precon={'max_iters': 1, 'solver': 'BLOCK_JACOBI'}, **kwargs)
Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon (Preconditioner, optional) –

  • **kwargs – Other AMGX solver options

__module__ = 'fipy.solvers.pyamgx.linearFGMRESSolver'
class fipy.solvers.pyamgx.LinearLUSolver(tolerance=1e-10, iterations=1000, precon=None)

Bases: fipy.solvers.scipy.scipySolver._ScipySolver

The LinearLUSolver solves a linear system of equations using LU-factorization. The LinearLUSolver is a wrapper class for the the Scipy scipy.sparse.linalg.splu module.

Create a Solver object.

Parameters

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon – Preconditioner to use. Not all solver suites support preconditioners.

__annotations__ = {}
__module__ = 'fipy.solvers.scipy.linearLUSolver'
fipy.solvers.pyamgx.LinearPCGSolver

alias of fipy.solvers.pyamgx.linearCGSolver.LinearCGSolver

Last updated on Jan 14, 2021. Created using Sphinx 3.4.3.