# ******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, sqrt
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_forces()
if optimizable.is_neb():
raise TypeError('NEB calculations cannot use the BFGSLineSearch'
' optimizer. Use BFGS or another optimizer.')
r = optimizable.get_positions()
r = r.reshape(-1)
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(len(optimizable) * 1e-10):
self.p /= (p_size / np.sqrt(len(optimizable) * 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_positions((r + dr).reshape(len(optimizable), -1))
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(len(self.optimizable) * 3, dtype=int)
if self.H is None:
self.H = eye(3 * len(self.optimizable))
# 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_positions(x.reshape(-1, 3))
self.function_calls += 1
# Scale the problem as SciPy uses I as initial Hessian.
return self.optimizable.get_potential_energy() / self.alpha
def fprime(self, x):
"""Gradient of the objective function for use of the optimizers"""
self.optimizable.set_positions(x.reshape(-1, 3))
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_forces().reshape(-1)
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, forces=None):
if self.logfile is None:
return
if forces is None:
forces = self.optimizable.get_forces()
fmax = sqrt((forces**2).sum(axis=1).max())
e = self.optimizable.get_potential_energy()
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