/usr/share/pyshared/ase/optimize/bfgslinesearch.py is in python-ase 3.6.0.2515-1.1.
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# ******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 numpy as np
from numpy import atleast_1d, eye, mgrid, argmin, zeros, shape, empty, \
squeeze, vectorize, asarray, absolute, sqrt, Inf, asfarray, isinf
from ase.utils.linesearch import LineSearch
from ase.optimize.optimize import Optimizer
from numpy import arange
# 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
import __builtin__
pymin = __builtin__.min
pymax = __builtin__.max
__version__="0.1"
class BFGSLineSearch(Optimizer):
def __init__(self, atoms, restart=None, logfile='-', maxstep=.2,
trajectory=None, c1=.23, c2=0.46, alpha=10., stpmax=50.,
use_free_energy=True):
"""Minimize a function using the BFGS algorithm.
Notes:
Optimize the function, f, whose gradient is given by fprime
using the quasi-Newton method of Broyden, Fletcher, Goldfarb,
and Shanno (BFGS) See Wright, and Nocedal 'Numerical
Optimization', 1999, pg. 198.
*See Also*:
scikits.openopt : SciKit which offers a unified syntax to call
this and other solvers.
"""
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
self.use_free_energy = use_free_energy
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
def read(self):
self.r0, self.g0, self.e0, self.task, self.H = self.load()
self.load_restart = True
def reset(self):
print 'reset'
self.H = None
self.r0 = None
self.g0 = None
self.e0 = None
self.rep_count = 0
def step(self, f):
atoms = self.atoms
from ase.neb import NEB
assert not isinstance(atoms, NEB)
r = atoms.get_positions()
r = r.reshape(-1)
g = -f.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 self.nsteps != 0:
p0_size = np.sqrt((p0 **2).sum())
delta_p = self.p/p_size + p0/p0_size
if p_size <= np.sqrt(len(atoms) * 1e-10):
self.p /= (p_size / np.sqrt(len(atoms)*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
atoms.set_positions((r+dr).reshape(len(atoms),-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.atoms) * 3, dtype=int)
if self.H is None:
self.H = eye(3 * len(self.atoms))
#self.B = np.linalg.inv(self.H)
return
else:
dr = r - r0
dg = g - g0
if not ((self.alpha_k > 0 and abs(np.dot(g,p0))-abs(np.dot(g0,p0)) < 0) \
or self.replay):
return
if self.no_update == True:
print 'skip update'
return
try: # this was handled in numeric, let it remaines 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
H0 = self.H
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.atoms.set_positions(x.reshape(-1, 3))
calc = self.atoms.get_calculator()
self.function_calls += 1
# Scale the problem as SciPy uses I as initial Hessian.
if self.use_free_energy:
try:
return calc.get_potential_energy(self.atoms,force_consistent=True) / self.alpha
except TypeError:
return calc.get_potential_energy(self.atoms) / self.alpha
else:
return calc.get_potential_energy(self.atoms) / self.alpha
def fprime(self, x):
"""Gradient of the objective function for use of the optimizers"""
self.atoms.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.
f = self.atoms.get_forces().reshape(-1)
return - f / self.alpha
def replay_trajectory(self, traj):
"""Initialize hessian from old trajectory."""
self.replay = True
if isinstance(traj, str):
from ase.io.trajectory import PickleTrajectory
traj = PickleTrajectory(traj, 'r')
atoms = traj[0]
r0 = None
g0 = None
for i in range(0, 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 wrap_function(function, args):
ncalls = [0]
def function_wrapper(x):
ncalls[0] += 1
return function(x, *args)
return ncalls, function_wrapper
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