# fmt: off
# ******NOTICE***************
# optimize.py module by Travis E. Oliphant
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************
import time
from typing import IO, Optional, Union
import numpy as np
from numpy import absolute, eye, isinf
from ase import Atoms
from ase.optimize.optimize import Optimizer
from ase.utils.linesearch import LineSearch
# These have been copied from Numeric's MLab.py
# I don't think they made the transition to scipy_core
# Modified from scipy_optimize
abs = absolute
pymin = min
pymax = max
__version__ = '0.1'
[docs]
class BFGSLineSearch(Optimizer):
def __init__(
self,
atoms: Atoms,
restart: Optional[str] = None,
logfile: Union[IO, str] = '-',
maxstep: float = None,
trajectory: Optional[str] = None,
c1: float = 0.23,
c2: float = 0.46,
alpha: float = 10.0,
stpmax: float = 50.0,
**kwargs,
):
"""Optimize atomic positions in the BFGSLineSearch algorithm, which
uses both forces and potential energy information.
Parameters
----------
atoms: :class:`~ase.Atoms`
The Atoms object to relax.
restart: str
JSON file used to store hessian matrix. If set, file with
such a name will be searched and hessian matrix stored will
be used, if the file exists.
trajectory: str
Trajectory file used to store optimisation path.
maxstep: float
Used to set the maximum distance an atom can move per
iteration (default value is 0.2 Angstroms).
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
kwargs : dict, optional
Extra arguments passed to
:class:`~ase.optimize.optimize.Optimizer`.
"""
if maxstep is None:
self.maxstep = self.defaults['maxstep']
else:
self.maxstep = maxstep
self.stpmax = stpmax
self.alpha = alpha
self.H = None
self.c1 = c1
self.c2 = c2
self.force_calls = 0
self.function_calls = 0
self.r0 = None
self.g0 = None
self.e0 = None
self.load_restart = False
self.task = 'START'
self.rep_count = 0
self.p = None
self.alpha_k = None
self.no_update = False
self.replay = False
Optimizer.__init__(self, atoms, restart, logfile, trajectory, **kwargs)
def read(self):
self.r0, self.g0, self.e0, self.task, self.H = self.load()
self.load_restart = True
def reset(self):
self.H = None
self.r0 = None
self.g0 = None
self.e0 = None
self.rep_count = 0
def step(self, forces=None):
optimizable = self.optimizable
if forces is None:
forces = optimizable.get_gradient().reshape(-1, 3)
r = optimizable.get_x()
g = -forces.reshape(-1) / self.alpha
p0 = self.p
self.update(r, g, self.r0, self.g0, p0)
# o,v = np.linalg.eigh(self.B)
e = self.func(r)
self.p = -np.dot(self.H, g)
p_size = np.sqrt((self.p**2).sum())
if p_size <= np.sqrt(optimizable.ndofs() / 3 * 1e-10):
self.p /= (p_size / np.sqrt(optimizable.ndofs() / 3 * 1e-10))
ls = LineSearch()
self.alpha_k, e, self.e0, self.no_update = \
ls._line_search(self.func, self.fprime, r, self.p, g, e, self.e0,
maxstep=self.maxstep, c1=self.c1,
c2=self.c2, stpmax=self.stpmax)
if self.alpha_k is None:
raise RuntimeError("LineSearch failed!")
dr = self.alpha_k * self.p
optimizable.set_x(r + dr)
self.r0 = r
self.g0 = g
self.dump((self.r0, self.g0, self.e0, self.task, self.H))
def update(self, r, g, r0, g0, p0):
self.I = eye(self.optimizable.ndofs(), dtype=int)
if self.H is None:
self.H = eye(self.optimizable.ndofs())
# self.B = np.linalg.inv(self.H)
return
else:
dr = r - r0
dg = g - g0
# self.alpha_k can be None!!!
if not (((self.alpha_k or 0) > 0 and
abs(np.dot(g, p0)) - abs(np.dot(g0, p0)) < 0) or
self.replay):
return
if self.no_update is True:
print('skip update')
return
try: # this was handled in numeric, let it remain for more safety
rhok = 1.0 / (np.dot(dg, dr))
except ZeroDivisionError:
rhok = 1000.0
print("Divide-by-zero encountered: rhok assumed large")
if isinf(rhok): # this is patch for np
rhok = 1000.0
print("Divide-by-zero encountered: rhok assumed large")
A1 = self.I - dr[:, np.newaxis] * dg[np.newaxis, :] * rhok
A2 = self.I - dg[:, np.newaxis] * dr[np.newaxis, :] * rhok
self.H = (np.dot(A1, np.dot(self.H, A2)) +
rhok * dr[:, np.newaxis] * dr[np.newaxis, :])
# self.B = np.linalg.inv(self.H)
def func(self, x):
"""Objective function for use of the optimizers"""
self.optimizable.set_x(x)
self.function_calls += 1
# Scale the problem as SciPy uses I as initial Hessian.
return self.optimizable.get_value() / self.alpha
def fprime(self, x):
"""Gradient of the objective function for use of the optimizers"""
self.optimizable.set_x(x)
self.force_calls += 1
# Remember that forces are minus the gradient!
# Scale the problem as SciPy uses I as initial Hessian.
forces = self.optimizable.get_gradient()
return - forces / self.alpha
def replay_trajectory(self, traj):
"""Initialize hessian from old trajectory."""
self.replay = True
from ase.utils import IOContext
with IOContext() as files:
if isinstance(traj, str):
from ase.io.trajectory import Trajectory
traj = files.closelater(Trajectory(traj, mode='r'))
r0 = None
g0 = None
for i in range(len(traj) - 1):
r = traj[i].get_positions().ravel()
g = - traj[i].get_forces().ravel() / self.alpha
self.update(r, g, r0, g0, self.p)
self.p = -np.dot(self.H, g)
r0 = r.copy()
g0 = g.copy()
self.r0 = r0
self.g0 = g0
def log(self, gradient):
if self.logfile is None:
return
fmax = self.optimizable.gradient_norm(gradient)
e = self.optimizable.get_value()
T = time.localtime()
name = self.__class__.__name__
w = self.logfile.write
if self.nsteps == 0:
w('%s %4s[%3s] %8s %15s %12s\n' %
(' ' * len(name), 'Step', 'FC', 'Time', 'Energy', 'fmax'))
w('%s: %3d[%3d] %02d:%02d:%02d %15.6f %12.4f\n'
% (name, self.nsteps, self.force_calls, T[3], T[4], T[5], e,
fmax))
self.logfile.flush()
def wrap_function(function, args):
ncalls = [0]
def function_wrapper(x):
ncalls[0] += 1
return function(x, *args)
return ncalls, function_wrapper
