13694664171

Committed 06 Mar 2025 08:46AM UTC coverage: 73.703% (+0.03%) from 73.673%

Build # 13694664171

Build Type

Pull #191

github

Committed by

web-flow

Commit Message

Merge 3e14e3c7c into 42cd363e1

Pull Request Pull Request #191: Fix orthgonality loss in Davidson using Gram-Schmidt reorthogonalisation

Run Details

1120 of 1808 branches covered (61.95%)

Branch coverage included in aggregate %.

39 of 47 new or added lines in 3 files covered. (82.98%)

2 existing lines in 2 files now uncovered.

7092 of 9334 relevant lines covered (75.98%)

191414.74 hits per line

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35.0

/adcc/solver/SolverStateBase.py

#!/usr/bin/env python3
## vi: tabstop=4 shiftwidth=4 softtabstop=4 expandtab
## ---------------------------------------------------------------------
##
## Copyright (C) 2018 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/>.
##
## ---------------------------------------------------------------------
from adcc.timings import Timer


class EigenSolverStateBase:
    def __init__(self, matrix):
        """Initialise an EigenSolverStateBase.

        Parameters
        ----------
        matrix
            Matrix to be diagonalised.
        """
        self.matrix = matrix
        self.eigenvalues = None           # Current eigenvalues
        self.eigenvectors = None          # Current eigenvectors
        self.residual_norms = None        # Current residual norms
        self.converged = False            # Flag whether iteration is converged
        self.n_iter = 0                   # Number of iterations
        self.n_applies = 0                # Number of applies
        self.reortho_triggers = []        # List of reorthogonalisation triggers
        self.timer = Timer()              # Construct a new timer

    def describe(self):
        text = ""

        problem = str(self.matrix)
        algorithm = getattr(self, "algorithm", "")

        if self.converged:
            conv = "converged"
        else:
            conv = "NOT CONVERGED"

        text += "+" + 60 * "-" + "+\n"
        text += "| {0:<41s}  {1:>15s} |\n".format(algorithm, conv)
        text += ("| {0:30s} n_iter={1:<3d}  n_applies={2:<5d} |\n"
                 "".format(problem[:30], self.n_iter, self.n_applies))
        text += ("| n_reortho={0:<7d}  max_overlap_before_reortho={1:<10s}   |\n"
                 "".format(len(self.reortho_triggers),
                           "{:<10.4E}".format(max(self.reortho_triggers))
                           if len(self.reortho_triggers) > 0 else "N/A"))
        text += "+" + 60 * "-" + "+\n"
        text += ("|  #     eigenvalue  res. norm       "
                 "dominant elements       |\n")

        body = "| {0:2d} {1:14.7g}  {2:9.4g}               TODO            |\n"
        for i, vec in enumerate(self.eigenvectors):
            text += body.format(i, self.eigenvalues[i], self.residual_norms[i])
        text += "+" + 60 * "-" + "+"
        return text

    def _repr_pretty_(self, pp, cycle):
        if cycle:
            pp.text("SolverState(...)")
        else:
            pp.text(self.describe())

1	#!/usr/bin/env python3
2	## vi: tabstop=4 shiftwidth=4 softtabstop=4 expandtab
3	## ---------------------------------------------------------------------
4	##
5	## Copyright (C) 2018 by the adcc authors
6	##
7	## This file is part of adcc.
8	##
9	## adcc is free software: you can redistribute it and/or modify
10	## it under the terms of the GNU General Public License as published
11	## by the Free Software Foundation, either version 3 of the License, or
12	## (at your option) any later version.
13	##
14	## adcc is distributed in the hope that it will be useful,
15	## but WITHOUT ANY WARRANTY; without even the implied warranty of
16	## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17	## GNU General Public License for more details.
18	##
19	## You should have received a copy of the GNU General Public License
20	## along with adcc. If not, see <http://www.gnu.org/licenses/>.
21	##
22	## ---------------------------------------------------------------------
23	from adcc.timings import Timer	2✔
24
25
26	class EigenSolverStateBase:	2✔
27	def __init__(self, matrix):	2✔
28	"""Initialise an EigenSolverStateBase.
29
30	Parameters
31	----------
32	matrix
33	Matrix to be diagonalised.
34	"""
35	self.matrix = matrix	2✔
36	self.eigenvalues = None # Current eigenvalues	2✔
37	self.eigenvectors = None # Current eigenvectors	2✔
38	self.residual_norms = None # Current residual norms	2✔
39	self.converged = False # Flag whether iteration is converged	2✔
40	self.n_iter = 0 # Number of iterations	2✔
41	self.n_applies = 0 # Number of applies	2✔
42	self.reortho_triggers = [] # List of reorthogonalisation triggers	2✔
43	self.timer = Timer() # Construct a new timer	2✔
44
45	def describe(self):	2✔
46	text = ""	×
47
48	problem = str(self.matrix)	×
49	algorithm = getattr(self, "algorithm", "")	×
50
51	if self.converged:	×
52	conv = "converged"	×
53	else:
54	conv = "NOT CONVERGED"	×
55
56	text += "+" + 60 * "-" + "+\n"	×
57	text += "\| {0:<41s} {1:>15s} \|\n".format(algorithm, conv)	×
58	text += ("\| {0:30s} n_iter={1:<3d} n_applies={2:<5d} \|\n"	×
59	"".format(problem[:30], self.n_iter, self.n_applies))
NEW 60	text += ("\| n_reortho={0:<7d} max_overlap_before_reortho={1:<10s} \|\n"	×
61	"".format(len(self.reortho_triggers),
62	"{:<10.4E}".format(max(self.reortho_triggers))
63	if len(self.reortho_triggers) > 0 else "N/A"))
64	text += "+" + 60 * "-" + "+\n"	×
65	text += ("\| # eigenvalue res. norm "	×
66	"dominant elements \|\n")
67
68	body = "\| {0:2d} {1:14.7g} {2:9.4g} TODO \|\n"	×
69	for i, vec in enumerate(self.eigenvectors):	×
70	text += body.format(i, self.eigenvalues[i], self.residual_norms[i])	×
71	text += "+" + 60 * "-" + "+"	×
72	return text	×
73
74	def _repr_pretty_(self, pp, cycle):	2✔
75	if cycle:	×
76	pp.text("SolverState(...)")	×
77	else:
78	pp.text(self.describe())	×

adc-connect / adcc / 13694664171

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