Source code for pymor.reductors.sor_irka

# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2019 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)

import numpy as np

from pymor.core.interfaces import BasicInterface
from pymor.models.iosys import SecondOrderModel
from pymor.reductors.interpolation import SOBHIReductor
from pymor.reductors.h2 import IRKAReductor, _poles_and_tangential_directions, _convergence_criterion


[docs]class SORIRKAReductor(BasicInterface): """SOR-IRKA reductor. Parameters ---------- fom The full-order |SecondOrderModel| to reduce. mu |Parameter|. """ def __init__(self, fom, mu=None): assert isinstance(fom, SecondOrderModel) self.fom = fom self.mu = fom.parse_parameter(mu) self.V = None self.W = None self._pg_reductor = None self.conv_crit = None self.sigmas = None self.R = None self.L = None self.errors = None
[docs] def reduce(self, r, sigma=None, b=None, c=None, rom0=None, tol=1e-4, maxit=100, num_prev=1, force_sigma_in_rhp=False, projection='orth', conv_crit='sigma', compute_errors=False, irka_options=None): r"""Reduce using SOR-IRKA. It uses IRKA as the intermediate reductor, to reduce from 2r to r poles. See Section 5.3.2 in [W12]_. Parameters ---------- r Order of the reduced order model. sigma Initial interpolation points (closed under conjugation). If `None`, interpolation points are log-spaced between 0.1 and 10. If `sigma` is an `int`, it is used as a seed to generate it randomly. Otherwise, it needs to be a one-dimensional array-like of length `r`. `sigma` and `rom0` cannot both be not `None`. b Initial right tangential directions. If `None`, if is chosen as all ones. If `b` is an `int`, it is used as a seed to generate it randomly. Otherwise, it needs to be a |VectorArray| of length `r` from `fom.B.source`. `b` and `rom0` cannot both be not `None`. c Initial left tangential directions. If `None`, if is chosen as all ones. If `c` is an `int`, it is used as a seed to generate it randomly. Otherwise, it needs to be a |VectorArray| of length `r` from `fom.Cp.range`. `c` and `rom0` cannot both be not `None`. rom0 Initial reduced order model. If `None`, then `sigma`, `b`, and `c` are used. Otherwise, it needs to be an |LTIModel| of order `r` and it is used to construct `sigma`, `b`, and `c`. tol Tolerance for the convergence criterion. maxit Maximum number of iterations. num_prev Number of previous iterations to compare the current iteration to. Larger number can avoid occasional cyclic behavior of IRKA. force_sigma_in_rhp If `False`, new interpolation are reflections of the current reduced order model's poles. Otherwise, only the poles in the left half-plane are reflected. projection Projection method: - `'orth'`: projection matrices are orthogonalized with respect to the Euclidean inner product - `'biorth'`: projection matrices are biorthogolized with respect to the E product conv_crit Convergence criterion: - `'sigma'`: relative change in interpolation points - `'h2'`: relative :math:`\mathcal{H}_2` distance of reduced-order models compute_errors Should the relative :math:`\mathcal{H}_2`-errors of intermediate reduced order models be computed. .. warning:: Computing :math:`\mathcal{H}_2`-errors is expensive. Use this option only if necessary. irka_options Dict of options for IRKAReductor.reduce. Returns ------- rom Reduced-order |SecondOrderModel|. """ fom = self.fom if not fom.cont_time: raise NotImplementedError assert 0 < r < fom.order assert isinstance(num_prev, int) and num_prev >= 1 assert projection in ('orth', 'biorth') assert conv_crit in ('sigma', 'h2') assert irka_options is None or isinstance(irka_options, dict) if not irka_options: irka_options = {} # initial interpolation points and tangential directions assert sigma is None or isinstance(sigma, int) or len(sigma) == r assert b is None or isinstance(b, int) or b in fom.B.source and len(b) == r assert c is None or isinstance(c, int) or c in fom.Cp.range and len(c) == r assert (rom0 is None or isinstance(rom0, SecondOrderModel) and rom0.order == r and rom0.B.source == fom.B.source and rom0.Cp.range == fom.Cp.range) assert sigma is None or rom0 is None assert b is None or rom0 is None assert c is None or rom0 is None if rom0 is not None: with self.logger.block('Intermediate reduction ...'): irka_reductor = IRKAReductor(rom0.to_lti()) rom_r = irka_reductor.reduce(r, **irka_options) poles, b, c = _poles_and_tangential_directions(rom_r) sigma = np.abs(poles.real) + poles.imag * 1j if force_sigma_in_rhp else -poles else: if sigma is None: sigma = np.logspace(-1, 1, r) elif isinstance(sigma, int): np.random.seed(sigma) sigma = np.abs(np.random.randn(r)) if b is None: b = fom.B.source.ones(r) elif isinstance(b, int): b = fom.B.source.random(r, distribution='normal', seed=b) if c is None: c = fom.Cp.range.ones(r) elif isinstance(c, int): c = fom.Cp.range.random(r, distribution='normal', seed=c) self.logger.info('Starting SOR-IRKA') self.conv_crit = [] self.sigmas = [np.array(sigma)] self.R = [b] self.L = [c] self.errors = [] if compute_errors else None self._pg_reductor = SOBHIReductor(fom, mu=self.mu) # main loop for it in range(maxit): # interpolatory reduced order model rom = self._pg_reductor.reduce(sigma, b, c, projection=projection) # reduction to a system with r poles with self.logger.block('Intermediate reduction ...'): irka_reductor = IRKAReductor(rom.to_lti()) rom_r = irka_reductor.reduce(r, **irka_options) # new interpolation points and tangential directions poles, b, c = _poles_and_tangential_directions(rom_r) sigma = np.abs(poles.real) + poles.imag * 1j if force_sigma_in_rhp else -poles self.sigmas.append(sigma) self.R.append(b) self.L.append(c) # compute convergence criterion if conv_crit == 'sigma': dist = _convergence_criterion(self.sigmas[:-num_prev-2:-1], conv_crit) self.conv_crit.append(dist) elif conv_crit == 'h2': if it == 0: rom_list = (num_prev + 1) * [None] rom_list[0] = rom self.conv_crit.append(np.inf) else: rom_list[1:] = rom_list[:-1] rom_list[0] = rom dist = _convergence_criterion(rom_list, conv_crit) self.conv_crit.append(dist) # report convergence self.logger.info(f'Convergence criterion in iteration {it + 1}: {self.conv_crit[-1]:e}') if compute_errors: if np.max(rom.poles().real) < 0: err = fom - rom rel_H2_err = err.h2_norm() / fom.h2_norm() else: rel_H2_err = np.inf self.errors.append(rel_H2_err) self.logger.info(f'Relative H2-error in iteration {it + 1}: {rel_H2_err:e}') # check if convergence criterion is satisfied if self.conv_crit[-1] < tol: break # final reduced order model rom = self._pg_reductor.reduce(sigma, b, c, projection=projection) self.V = self._pg_reductor.V self.W = self._pg_reductor.W return rom
[docs] def reconstruct(self, u): """Reconstruct high-dimensional vector from reduced vector `u`.""" return self._pg_reductor.reconstruct(u)