Source code for sfepy.linalg.eigen

from __future__ import absolute_import
import numpy as nm
import scipy.sparse as sp
from scipy.sparse.linalg import aslinearoperator
from scipy.linalg import eigvals_banded

from sfepy.base.base import get_default, output
from sfepy.linalg import infinity_norm
from six.moves import range

[docs]def sym_tri_eigen(diags, select_indices=None): """ Compute eigenvalues of a symmetric tridiagonal matrix using `scipy.linalg.eigvals_banded()`. """ if select_indices is not None: n = diags.shape[1] select_indices = nm.minimum(select_indices, n) eigs = eigvals_banded(diags, lower=True, select='i', select_range=select_indices) else: eigs = eigvals_banded(diags, lower=True, select='a') return eigs
[docs]def cg_eigs(mtx, rhs=None, precond=None, i_max=None, eps_r=1e-10, shift=None, select_indices=None, verbose=False, report_step=10): r""" Make several iterations of the conjugate gradients and estimate so the eigenvalues of a (sparse SPD) matrix (Lanczos algorithm). Parameters ---------- mtx : spmatrix or array The sparse matrix :math:`A`. precond : spmatrix or array, optional The preconditioner matrix. Any object that can be multiplied by vector can be passed. i_max : int The maximum number of the Lanczos algorithm iterations. eps_r : float The relative stopping tolerance. shift : float, optional Eigenvalue shift for non-SPD matrices. If negative, the shift is computed as :math:`|shift| ||A||_{\infty}`. select_indices : (min, max), optional If given, computed only the eigenvalues with indices `min <= i <= max`. verbose : bool Verbosity control. report_step : int If `verbose` is True, report in every `report_step`-th step. Returns ------- vec : array The approximate solution to the linear system. n_it : int The number of CG iterations used. norm_rs : array Convergence history of residual norms. eigs : array The approximate eigenvalues sorted in ascending order. """ n_row = mtx.shape[0] norm = nm.linalg.norm rhs = get_default(rhs, nm.random.rand(n_row)) i_max = get_default(i_max, min(n_row, 100)) if shift is not None: if shift < 0.0: mtx_norm = infinity_norm(mtx) output('matrix max norm:', mtx_norm, verbose=verbose) shift = abs(shift) * mtx_norm output('eigenvalue shift:', shift, verbose=verbose) mtx = mtx + shift * sp.eye(n_row, n_row, dtype=mtx.dtype) mtx = aslinearoperator(mtx) lambda_max = 0 lambda_min = 0 econd = 1.0 x0 = nm.zeros_like(rhs) r = r0 = rhs # The diagonals (0, 1) in two rows. diags = nm.empty((2, i_max + 1), dtype=mtx.dtype) diags[0, 0] = 0 if precond is None: z0 = r0 else: z0 = precond * r0 x = x0 z = z0 p = nm.zeros_like(rhs) beta = 0.0 rho0 =, r0) norm_r0 = norm(r0); if verbose: output('%5d lambda: %e %e cond: %e |R|: %e\n' % (0, 0, 0, 0, norm_r0)) norm_rs = [norm_r0] for ii in range(i_max): p = z + beta * p q = mtx * p alpha = rho0 /, q) if (not nm.isfinite(alpha)) or abs(alpha) < 1e-16: output('precision limit reached!') ii -= 1 break x = x + alpha * p r = r - alpha * q if precond is None: z = r else: z = precond * r norm_r = norm(r) norm_rs.append(norm_r) rho1 =, r) beta = rho1 / rho0 # Lanczos diags[0, ii] += 1.0 / alpha diags[1, ii] = - nm.sqrt(beta) / alpha diags[0, ii+1] = beta / alpha if verbose and (ii > 0): if (ii % report_step) == 0: eigs = sym_tri_eigen(diags[:, :ii+1], select_indices=select_indices) if select_indices is None: lambda_min, lambda_max = eigs[0], eigs[-1] econd = lambda_max / lambda_min output('%5d lambda: %e %e cond: %e |R|: %e\n' % (ii, lambda_min, lambda_max, econd, norm_r)) else: output('%5d |R|: %e\n' % (ii, norm_r)) if (norm_r / norm_r0) < eps_r: output('converged on %d iters, |Ri|/|R0|: %e, econd: %e\n' % (ii, norm_r / norm_r0, econd), verbose=verbose) break rho0 = rho1 eigs = sym_tri_eigen(diags[:, :ii+1], select_indices=select_indices) if verbose and (select_indices is None): lambda_min, lambda_max = eigs[0], eigs[-1] econd = lambda_max / lambda_min output('min: %e max: %e cond: %e\n' % (lambda_min, lambda_max, econd)) if shift is not None: eigs -= shift return x, ii, nm.array(norm_rs), eigs
[docs]def arpack_eigs(mtx, nev=1, which='SM'): """ Calculate several eigenvalues and corresponding eigenvectors of a matrix using ARPACK from SciPy. The eigenvalues are sorted in ascending order. """ from scipy.sparse.linalg.interface import aslinearoperator from scipy.sparse.linalg.eigen.arpack import speigs matvec = aslinearoperator(mtx).matvec eigs, vecs = speigs.ARPACK_eigs(matvec, mtx.shape[0], nev=nev, which=which) ii = nm.argsort(eigs) eigs = eigs[ii] vecs = vecs[:,ii] return eigs, vecs