96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
|
"""
|
||
|
|
Solvers for eigenvalue / eigenvector problems
|
||
|
|
"""
|
||
|
|
from typing import Tuple, List
|
||
|
|
import numpy
|
||
|
|
from numpy.linalg import norm
|
||
|
|
from scipy import sparse
|
||
|
|
import scipy.sparse.linalg as spalg
|
||
|
|
|
||
|
|
|
||
|
|
def power_iteration(operator: sparse.spmatrix,
|
||
|
|
guess_vector: numpy.ndarray = None,
|
||
|
|
iterations: int = 20,
|
||
|
|
) -> Tuple[complex, numpy.ndarray]:
|
||
|
|
"""
|
||
|
|
Use power iteration to estimate the dominant eigenvector of a matrix.
|
||
|
|
|
||
|
|
:param operator: Matrix to analyze.
|
||
|
|
:param guess_vector: Starting point for the eigenvector. Default is a randomly chosen vector.
|
||
|
|
:param iterations: Number of iterations to perform. Default 20.
|
||
|
|
:return: (Largest-magnitude eigenvalue, Corresponding eigenvector estimate)
|
||
|
|
"""
|
||
|
|
if numpy.any(numpy.equal(guess_vector, None)):
|
||
|
|
v = numpy.random.rand(operator.shape[0])
|
||
|
|
else:
|
||
|
|
v = guess_vector
|
||
|
|
|
||
|
|
for _ in range(iterations):
|
||
|
|
v = operator @ v
|
||
|
|
v /= norm(v)
|
||
|
|
|
||
|
|
lm_eigval = v.conj() @ operator @ v
|
||
|
|
return lm_eigval, v
|
||
|
|
|
||
|
|
|
||
|
|
def rayleigh_quotient_iteration(operator: sparse.spmatrix,
|
||
|
|
guess_vector: numpy.ndarray,
|
||
|
|
iterations: int = 40,
|
||
|
|
tolerance: float = 1e-13,
|
||
|
|
) -> Tuple[complex, numpy.ndarray]:
|
||
|
|
"""
|
||
|
|
Use Rayleigh quotient iteration to refine an eigenvector guess.
|
||
|
|
|
||
|
|
:param operator: Matrix to analyze.
|
||
|
|
:param guess_vector: Eigenvector to refine.
|
||
|
|
:param iterations: Maximum number of iterations to perform. Default 40.
|
||
|
|
:param tolerance: Stop iteration if (A - I*eigenvalue) @ v < tolerance.
|
||
|
|
Default 1e-13.
|
||
|
|
:return: (eigenvalue, eigenvector)
|
||
|
|
"""
|
||
|
|
v = guess_vector
|
||
|
|
for _ in range(iterations):
|
||
|
|
eigval = v.conj() @ operator @ v
|
||
|
|
if norm(operator @ v - eigval * v) < tolerance:
|
||
|
|
break
|
||
|
|
v = spalg.spsolve(operator - eigval * sparse.eye(operator.shape[0]), v)
|
||
|
|
v /= norm(v)
|
||
|
|
|
||
|
|
return eigval, v
|
||
|
|
|
||
|
|
|
||
|
|
def signed_eigensolve(operator: sparse.spmatrix,
|
||
|
|
how_many: int,
|
||
|
|
negative: bool = False,
|
||
|
|
) -> Tuple[numpy.ndarray, numpy.ndarray]:
|
||
|
|
"""
|
||
|
|
Find the largest-magnitude positive-only (or negative-only) eigenvalues and
|
||
|
|
eigenvectors of the provided matrix.
|
||
|
|
|
||
|
|
:param operator: Matrix to analyze.
|
||
|
|
:param how_many: How many eigenvalues to find.
|
||
|
|
:param negative: Whether to find negative-only eigenvalues.
|
||
|
|
Default False (positive only).
|
||
|
|
:return: (sorted list of eigenvalues, 2D ndarray of corresponding eigenvectors)
|
||
|
|
eigenvectors[:, k] corresponds to the k-th eigenvalue
|
||
|
|
"""
|
||
|
|
# Use power iteration to estimate the dominant eigenvector
|
||
|
|
lm_eigval, _ = power_iteration(operator)
|
||
|
|
|
||
|
|
'''
|
||
|
|
Shift by the absolute value of the largest eigenvalue, then find a few of the
|
||
|
|
largest-magnitude (shifted) eigenvalues. A positive shift ensures that we find the
|
||
|
|
largest _positive_ eigenvalues, since any negative eigenvalues will be shifted to the
|
||
|
|
range 0 >= neg_eigval + abs(lm_eigval) > abs(lm_eigval)
|
||
|
|
'''
|
||
|
|
if negative:
|
||
|
|
shifted_operator = operator - abs(lm_eigval) * sparse.eye(operator.shape[0])
|
||
|
|
else:
|
||
|
|
shifted_operator = operator + abs(lm_eigval) * sparse.eye(operator.shape[0])
|
||
|
|
|
||
|
|
eigenvalues, eigenvectors = spalg.eigs(shifted_operator, which='LM', k=how_many, ncv=50)
|
||
|
|
|
||
|
|
k = eigenvalues.argsort()
|
||
|
|
return eigenvalues[k], eigenvectors[:, k]
|
||
|
|
|