-
Notifications
You must be signed in to change notification settings - Fork 19
/
test_lanczos.py
109 lines (92 loc) · 4.44 KB
/
test_lanczos.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
#!/usr/bin/env python3
## vi: tabstop=4 shiftwidth=4 softtabstop=4 expandtab
## ---------------------------------------------------------------------
##
## Copyright (C) 2020 by the adcc authors
##
## This file is part of adcc.
##
## adcc is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published
## by the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## adcc is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with adcc. If not, see <http://www.gnu.org/licenses/>.
##
## ---------------------------------------------------------------------
import adcc
import unittest
from pytest import approx
from adcc import LazyMp
from adcc.AdcMatrix import AdcMatrixShifted
from adcc.solver.lanczos import default_print as la_print, lanczos
from adcc.solver.preconditioner import JacobiPreconditioner
from adcc.solver.conjugate_gradient import (IterativeInverse,
default_print as cg_print)
from adcc.solver.explicit_symmetrisation import IndexSpinSymmetrisation
from adcc.testdata.cache import cache
class TestSolverLanczos(unittest.TestCase):
def test_adc2_singlets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
# Solve for singlets
guesses = adcc.guesses_singlet(matrix, n_guesses=5, block="ph")
res = lanczos(matrix, guesses, n_ep=5, which="SM")
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"][:5]
assert res.converged
assert res.eigenvalues == approx(ref_singlets)
def test_adc2_triplets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
# Solve for triplets
guesses = adcc.guesses_triplet(matrix, n_guesses=6, block="ph")
res = lanczos(matrix, guesses, n_ep=6, which="SM")
ref_triplets = refdata["adc2"]["triplet"]["eigenvalues"][:6]
assert res.converged
assert res.eigenvalues == approx(ref_triplets)
def test_adc2_shift_invert_singlets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
conv_tol = 1e-5
shift = -0.5
# Construct shift and inverted matrix:
shinv = IterativeInverse(AdcMatrixShifted(matrix, shift),
conv_tol=conv_tol / 10,
Pinv=JacobiPreconditioner,
callback=cg_print)
# Solve for singlets
guesses = adcc.guesses_singlet(matrix, n_guesses=5, block="ph")
symm = IndexSpinSymmetrisation(matrix, enforce_spin_kind="singlet")
res = lanczos(shinv, guesses, n_ep=5, callback=la_print,
explicit_symmetrisation=symm)
assert res.converged
# Undo spectral transformation and compare
eigenvalues = sorted(1 / res.eigenvalues - shift)
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"][:5]
assert eigenvalues == approx(ref_singlets)
def test_adc2_shift_invert_triplets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
conv_tol = 1e-5
shift = -0.5
# Construct shift and inverted matrix:
shinv = IterativeInverse(AdcMatrixShifted(matrix, shift),
conv_tol=conv_tol / 10,
Pinv=JacobiPreconditioner,
callback=cg_print)
# Solve for triplets
guesses = adcc.guesses_triplet(matrix, n_guesses=5, block="ph")
symm = IndexSpinSymmetrisation(matrix, enforce_spin_kind="triplet")
res = lanczos(shinv, guesses, n_ep=5, callback=la_print,
explicit_symmetrisation=symm)
assert res.converged
# Undo spectral transformation and compare
eigenvalues = sorted(1 / res.eigenvalues - shift)
ref_triplets = refdata["adc2"]["triplet"]["eigenvalues"][:5]
assert eigenvalues == approx(ref_triplets)