Source code for sfepy.solvers.eigen

from __future__ import absolute_import

import numpy as nm
import scipy.sparse as sps

from sfepy.base.base import output, get_default, try_imports, Struct
from sfepy.base.timing import Timer
from sfepy.solvers.solvers import Solver, EigenvalueSolver
import six
from six.moves import range

[docs]def eig(mtx_a, mtx_b=None, n_eigs=None, eigenvectors=True, return_time=None, method='eig.scipy', **ckwargs): """ Utility function that constructs an eigenvalue solver given by `method`, calls it and returns solution. """ kwargs = {'name' : 'aux', 'kind' : method} kwargs.update(ckwargs) conf = Struct(**kwargs) solver = Solver.any_from_conf(conf) status = {} out = solver(mtx_a, mtx_b, n_eigs, eigenvectors, status) if return_time is not None: return_time[0] = status['time'] return out
[docs]def standard_call(call): """ Decorator handling argument preparation and timing for eigensolvers. """ def _standard_call(self, mtx_a, mtx_b=None, n_eigs=None, eigenvectors=None, status=None, conf=None, **kwargs): timer = Timer(start=True) conf = get_default(conf, self.conf) mtx_a = get_default(mtx_a, self.mtx_a) mtx_b = get_default(mtx_b, self.mtx_b) n_eigs = get_default(n_eigs, self.n_eigs) eigenvectors = get_default(eigenvectors, self.eigenvectors) status = get_default(status, self.status) if n_eigs == 0: result = self._ret_zero(mtx_a, eigenvectors=eigenvectors) else: result = call(self, mtx_a, mtx_b, n_eigs, eigenvectors, status, conf, **kwargs) elapsed = timer.stop() if status is not None: status['time'] = elapsed return result return _standard_call
[docs]class ScipyEigenvalueSolver(EigenvalueSolver): """ SciPy-based solver for both dense and sparse problems. The problem is consirered sparse if `n_eigs` argument is not None. """ name = 'eig.scipy' _parameters = [ ('method', "{'eig', 'eigh', 'eigs', 'eigsh'}", 'eigs', False, """The method for solving general or symmetric eigenvalue problems: for dense problems :func:`eig()` or :func:`eigh()` can be used, for sparse problems :func:`eigs()` or :func:`eigsh()` should be used."""), ('which', "'LM' | 'SM' | 'LR' | 'SR' | 'LI' | 'SI'", 'SM', False, """Which eigenvectors and eigenvalues to find, see :func:`scipy.sparse.linalg.eigs()` or :func:`scipy.sparse.linalg.eigsh()`. For dense problmes, only 'LM' and 'SM' can be used"""), ('*', '*', None, False, 'Additional parameters supported by the method.'), ] def __init__(self, conf, **kwargs): EigenvalueSolver.__init__(self, conf, **kwargs) import scipy.linalg as sla self.sla = sla aux = try_imports(['import scipy.sparse.linalg as ssla'], 'cannot import scipy sparse eigenvalue solvers!') self.ssla = aux['ssla'] @standard_call def __call__(self, mtx_a, mtx_b=None, n_eigs=None, eigenvectors=None, status=None, conf=None): kwargs = self.build_solver_kwargs(conf) if conf.method in ('eig', 'eigh'): mtx_a, mtx_b = self._to_array(mtx_a, mtx_b) if conf.method == 'eig': out = self.sla.eig(mtx_a, mtx_b, right=eigenvectors, **kwargs) else: out = self.sla.eigh(mtx_a, mtx_b, eigvals_only=not eigenvectors, **kwargs) else: eig = self.ssla.eigs if conf.method == 'eigs' else self.ssla.eigsh out = eig(mtx_a, M=mtx_b, k=n_eigs, which=conf.which, return_eigenvectors=eigenvectors, **kwargs) if eigenvectors: eigs = out[0] else: eigs = out if nm.iscomplexobj(eigs): ii = nm.argsort(nm.linalg.norm(eigs[:, None], axis=1)) else: ii = nm.argsort(eigs) if n_eigs is not None and (conf.method in ('eig', 'eigh')): if conf.which == 'SM': ii = ii[:n_eigs] elif conf.which == 'LM': ii = ii[:-n_eigs-1:-1] else: raise ValueError("only 'LM' or 'SM' can be used with dense" " problems! (%s)" % conf.which) if eigenvectors: mtx_ev = out[1][:, ii] out = (eigs[ii], mtx_ev) else: out = eigs[ii] return out
[docs]class ScipySGEigenvalueSolver(EigenvalueSolver): """ SciPy-based solver for dense symmetric problems. """ name = 'eig.sgscipy' def __init__(self, conf, **kwargs): EigenvalueSolver.__init__(self, conf, **kwargs) try: import scipy.linalg.lapack as llapack except ImportError: import scipy.lib.lapack as llapack self.llapack = llapack @standard_call def __call__(self, mtx_a, mtx_b=None, n_eigs=None, eigenvectors=None, status=None, conf=None): """ Notes ----- Eigenvectors argument is ignored, as they are computed always. """ ll = self.llapack mtx_a, mtx_b = self._to_array(mtx_a, mtx_b) if nm.iscomplexobj(mtx_a): if mtx_b is None: fun = ll.get_lapack_funcs(['heev'], arrays=(mtx_a,))[0] else: fun = ll.get_lapack_funcs(['hegv'], arrays=(mtx_a,))[0] else: if mtx_b is None: fun = ll.get_lapack_funcs(['syev'], arrays=(mtx_a,))[0] else: fun = ll.get_lapack_funcs(['sygv'], arrays=(mtx_a,))[0] if mtx_b is None: out = fun(mtx_a) else: out = fun(mtx_a, mtx_b) # Fix output order of scipy.linalg.lapack functions. if out[0].ndim == 2: out = (out[1], out[0]) + out[2:] if not eigenvectors: if n_eigs is None: out = out[0] else: out = out[0][:n_eigs] else: if n_eigs is None: out = out[:-1] else: out = (out[0][:n_eigs], out[1][:, :n_eigs]) return out
[docs]class LOBPCGEigenvalueSolver(EigenvalueSolver): """ SciPy-based LOBPCG solver for sparse symmetric problems. """ name = 'eig.scipy_lobpcg' _parameters = [ ('i_max', 'int', 20, False, 'The maximum number of iterations.'), ('eps_a', 'float', None, False, 'The absolute tolerance for the convergence.'), ('largest', 'bool', True, False, 'If True, solve for the largest eigenvalues, otherwise the smallest.'), ('precond', '{dense matrix, sparse matrix, LinearOperator}', None, False, 'The preconditioner.'), ] def __init__(self, conf, **kwargs): EigenvalueSolver.__init__(self, conf, **kwargs) from scipy.sparse.linalg import lobpcg self.lobpcg = lobpcg @standard_call def __call__(self, mtx_a, mtx_b=None, n_eigs=None, eigenvectors=None, status=None, conf=None): if n_eigs is None: n_eigs = mtx_a.shape[0] else: n_eigs = min(n_eigs, mtx_a.shape[0]) x = nm.zeros((mtx_a.shape[0], n_eigs), dtype=nm.float64) x[:n_eigs] = nm.eye(n_eigs, dtype=nm.float64) out = self.lobpcg(mtx_a, x, mtx_b, M=conf.precond, tol=conf.eps_a, maxiter=conf.i_max, largest=conf.largest, verbosityLevel=conf.verbose) if not eigenvectors: out = out[0] return out
[docs]def init_slepc_args(): try: import sys, slepc4py except ImportError: return argv = [arg for arg in sys.argv if arg not in ['-h', '--help']] slepc4py.init(argv)
[docs]class SLEPcEigenvalueSolver(EigenvalueSolver): """ General SLEPc eigenvalue problem solver. """ name = 'eig.slepc' _parameters = [ ('method', 'str', 'krylovschur', False, 'The actual solver to use.'), ('problem', 'str', 'gnhep', False, """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_max', 'int', 20, False, 'The maximum number of iterations.'), ('eps', 'float', None, False, 'The convergence tolerance.'), ('conv_test', '{"abs", "rel", "norm", "user"}, ', 'abs', False, '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'}""", 'largest_magnitude', False, 'Which eigenvectors and eigenvalues to find.'), ('*', '*', None, False, 'Additional parameters supported by the method.'), ] def __init__(self, conf, comm=None, context=None, **kwargs): if comm is None: init_slepc_args() from petsc4py import PETSc as petsc from slepc4py import SLEPc as slepc EigenvalueSolver.__init__(self, conf, petsc=petsc, slepc=slepc, comm=comm, context=context, **kwargs)
[docs] def create_eps(self, options=None, comm=None): optDB = self.petsc.Options() if options is not None: for key, val in six.iteritems(options): optDB[key] = val es = self.slepc.EPS() es.create(comm) return es
[docs] def create_petsc_matrix(self, mtx, comm=None): if mtx is None or isinstance(mtx, self.petsc.Mat): pmtx = mtx else: mtx = sps.csr_matrix(mtx) pmtx = self.petsc.Mat() pmtx.createAIJ(mtx.shape, csr=(mtx.indptr, mtx.indices,, comm=comm) return pmtx
@standard_call def __call__(self, mtx_a, mtx_b=None, n_eigs=None, eigenvectors=None, status=None, conf=None, comm=None, context=None): solver_kwargs = self.build_solver_kwargs(conf) pmtx_a = self.create_petsc_matrix(mtx_a, comm=comm) pmtx_b = self.create_petsc_matrix(mtx_b, comm=comm) es = self.create_eps(options=solver_kwargs, comm=comm) es.setType(conf.method) es.setProblemType(getattr(es.ProblemType, conf.problem.upper())) es.setDimensions(nev=n_eigs) es.setTolerances(tol=conf.eps, max_it=conf.i_max) es.setOperators(pmtx_a, pmtx_b) es.setConvergenceTest(getattr(es.Conv, conf.conv_test.upper())) es.setWhichEigenpairs(getattr(es.Which, conf.which.upper())) es.setFromOptions() es.solve() n_converged = es.getConverged() if status is not None: status['n_iter'] = es.getIterationNumber() status['n_converged'] = n_converged if not eigenvectors: out = nm.array([es.getEigenvalue(ii) for ii in range(n_converged)]) else: vr, vi = pmtx_a.createVecs() eigs = [] vrs, vis = [], [] is_real = True for ii in range(n_converged): val = es.getEigenpair(ii, vr, vi) eigs.append(val if val.imag != 0 else val.real) vrs.append(vr.getArray().copy()) vis.append(vi.getArray().copy()) if is_real and nm.sum(nm.abs(vis[-1])) > 0.0: is_real = False eigs = nm.array(eigs) vecs = nm.array(vrs).T if not is_real: vecs += 1j * nm.array(vis) out = (eigs, vecs) return out
[docs]class MatlabEigenvalueSolver(EigenvalueSolver): """ Matlab eigenvalue problem solver. """ name = 'eig.matlab' _parameters = [ ('method', """{'eig', 'eigs', None}""", 'eigs', False, """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'}""", 'balance', False, 'The balance option for eig().'), ('algorithm', """{'chol', 'qz'}""", 'chol', False, 'The algorithm option for eig().'), ('which', """{'lm', 'sm', 'la', 'sa', 'be' 'lr', 'sr', 'li', 'si', sigma}""", 'lm', False, 'Which eigenvectors and eigenvalues to find with eigs().'), ('*', '*', None, False, 'Additional parameters supported by eigs().'), ] def __init__(self, conf, comm=None, context=None, **kwargs): import matlab.engine as me EigenvalueSolver.__init__(self, conf, me=me, context=context, **kwargs) @standard_call def __call__(self, mtx_a, mtx_b=None, n_eigs=None, eigenvectors=None, status=None, conf=None, comm=None, context=None): import os import shutil import tempfile import as sio solver_kwargs = self.build_solver_kwargs(conf) dirname = tempfile.mkdtemp() mtx_filename = os.path.join(dirname, 'matrices.mat') eigs_filename = os.path.join(dirname, 'eigs.mat') sio.savemat(mtx_filename, { 'A' : mtx_a, 'B' : mtx_b if mtx_b is not None else 'None', 'n_eigs' : n_eigs if n_eigs is not None else 'None', 'eigenvectors' : (eigenvectors if eigenvectors is not None else False), 'method' : conf.method, 'balance' : conf.balance, 'algorithm' : conf.algorithm, 'which' : conf.which, 'verbose' : conf.verbose, 'eigs_options' : solver_kwargs, }) eng = eng.matlab_eig(mtx_filename, eigs_filename) eng.quit() evp = sio.loadmat(eigs_filename) shutil.rmtree(dirname) out = evp['vals'][:, 0] if eigenvectors: out = (out, evp['vecs']) return out