sfepy.solvers.eigen module

class sfepy.solvers.eigen.LOBPCGEigenvalueSolver(conf, **kwargs)

SciPy-based LOBPCG solver for sparse symmetric problems.

Kind: ‘eig.scipy_lobpcg’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

i_maxint (default: 20)

The maximum number of iterations.

eps_afloat

The absolute tolerance for the convergence.

largestbool (default: True)

If True, solve for the largest eigenvalues, otherwise the smallest.

precond{dense matrix, sparse matrix, LinearOperator}

The preconditioner.

name = 'eig.scipy_lobpcg'

class sfepy.solvers.eigen.MatlabEigenvalueSolver(conf, comm=None, context=None, **kwargs)

Matlab eigenvalue problem solver.

Kind: ‘eig.matlab’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

method{‘eig’, ‘eigs’, None} (default: ‘eigs’)

The solution method. Note that eig() function cannot be used for all inputs. If n_eigs is not None, eigs() is used regardless of this parameter.

balance{‘balance’, ‘nobalance’} (default: ‘balance’)

The balance option for eig().

algorithm{‘chol’, ‘qz’} (default: ‘chol’)

The algorithm option for eig().

which{‘lm’, ‘sm’, ‘la’, ‘sa’, ‘be’ ‘lr’, ‘sr’, ‘li’, ‘si’, sigma} (default: ‘lm’)

Which eigenvectors and eigenvalues to find with eigs().

**

Additional parameters supported by eigs().

name = 'eig.matlab'

solver_call(mtx_filename, eigs_filename, which=None)

class sfepy.solvers.eigen.OctaveEigenvalueSolver(conf, comm=None, context=None, **kwargs)

Octave eigenvalue problem solver.

Kind: ‘eig.octave’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

name = 'eig.octave'

solver_call(mtx_filename, eigs_filename, which)

class sfepy.solvers.eigen.PrimmeEigenvalueSolver(conf, comm=None, context=None, **kwargs)

PRIMME eigenvalue problem solver.

https://github.com/primme/primme

Installation: pip install primme

Kind: ‘eig.primme’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

which{‘LM’, ‘SM’, ‘LA’, ‘SA’, ‘CLT’, ‘CGT’} (default: ‘LM’)

Which eigenvectors and eigenvalues to find.

sigmafloat

Find eigenvalues near sigma.

maxiterint

Maximum number of iterations.

tolfloat (default: 0)

Tolerance for eigenpairs (stopping criterion).

**

Additional parameters supported by eigsh().

name = 'eig.primme'

class sfepy.solvers.eigen.SLEPcEigenvalueSolver(conf, comm=None, context=None, **kwargs)

General SLEPc eigenvalue problem solver.

Kind: ‘eig.slepc’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

methodstr (default: ‘krylovschur’)

The actual solver to use.

problemstr (default: ‘gnhep’)

The problem type: Hermitian (hep), non-Hermitian (nhep), generalized Hermitian (ghep), generalized non-Hermitian (gnhep), generalized non-Hermitian with positive semi-definite B (pgnhep), and generalized Hermitian-indefinite (ghiep).

i_maxint (default: 20)

The maximum number of iterations.

epsfloat

The convergence tolerance.

conv_test{“abs”, “rel”, “norm”, “user”}, (default: ‘abs’)

The type of convergence test.

which{‘largest_magnitude’, ‘smallest_magnitude’,

‘largest_real’, ‘smallest_real’, ‘largest_imaginary’, ‘smallest_imaginary’, ‘target_magnitude’, ‘target_real’, ‘target_imaginary’, ‘all’, ‘which_user’} (default: ‘largest_magnitude’) Which eigenvectors and eigenvalues to find.

**

Additional parameters supported by the method.

create_eps(options=None, comm=None)

create_petsc_matrix(mtx, comm=None)

name = 'eig.slepc'

class sfepy.solvers.eigen.ScipyEigenvalueSolver(conf, **kwargs)

SciPy-based solver for both dense and sparse problems.

The problem is consirered sparse if n_eigs argument is not None.

Kind: ‘eig.scipy’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

method{‘eig’, ‘eigh’, ‘eigs’, ‘eigsh’} (default: ‘eigs’)

The method for solving general or symmetric eigenvalue problems: for dense problems eig() or eigh() can be used, for sparse problems eigs() or eigsh() should be used.

which‘LM’ | ‘SM’ | ‘LR’ | ‘SR’ | ‘LI’ | ‘SI’ (default: ‘SM’)

Which eigenvectors and eigenvalues to find, see scipy.sparse.linalg.eigs() or scipy.sparse.linalg.eigsh(). For dense problmes, only ‘LM’ and ‘SM’ can be used

linear_solver({‘ls.cholesky’, ‘ls.mumps’, …}, ls_conf)

The method used to construct an inverse linear operator. If None, the eigenvalue solver will solve the linear system internally.

**

Additional parameters supported by the method.

name = 'eig.scipy'

class sfepy.solvers.eigen.ScipySGEigenvalueSolver(conf, **kwargs)

SciPy-based solver for dense symmetric problems.

Kind: ‘eig.sgscipy’

For common configuration parameters, see Solver.

Specific configuration parameters:

Parameters:

name = 'eig.sgscipy'

sfepy.solvers.eigen.eig(mtx_a, mtx_b=None, n_eigs=None, eigenvectors=True, return_time=None, solver_kind='eig.scipy', **ckwargs)

Utility function that constructs an eigenvalue solver given by solver_kind, calls it and returns its output.

sfepy.solvers.eigen.init_slepc_args()

sfepy.solvers.eigen.standard_call(call)

Decorator handling argument preparation and timing for eigensolvers.