Chimie Théorique » QMX

# -*- coding: utf-8 -*-

import numpy as np

from numpy.linalg import eigh, solve

from ase.optimize.optimize import Optimizer

class BFGS(Optimizer):

    def __init__(self, atoms, restart=None, logfile='-', trajectory=None,

                 maxstep=None):

        """BFGS optimizer.

        Parameters:

        restart: string

            Pickle 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: string

            Pickle file used to store trajectory of atomic movement.

        maxstep: float

            Used to set the maximum distance an atom can move per

            iteration (default value is 0.04 Å).

        """

        Optimizer.__init__(self, atoms, restart, logfile, trajectory)

        if maxstep is not None:

            if maxstep > 1.0:

                raise ValueError('You are using a much too large value for ' +

                                 'the maximum step size: %.1f Å' % maxstep)

            self.maxstep = maxstep

    def initialize(self):

        self.H = None

        self.r0 = None

        self.f0 = None

        self.maxstep = 0.04

    def read(self):

        self.H, self.r0, self.f0, self.maxstep = self.load()

    def step(self, f):

        atoms = self.atoms

        r = atoms.get_positions()

        f = f.reshape(-1)

        self.update(r.flat, f, self.r0, self.f0)

        omega, V = eigh(self.H)

        dr = np.dot(V, np.dot(f, V) / np.fabs(omega)).reshape((-1, 3))

        #dr = solve(self.H, f).reshape((-1, 3))

        steplengths = (dr**2).sum(1)**0.5

        dr = self.determine_step(dr, steplengths)

        atoms.set_positions(r + dr)

        self.r0 = r.flat.copy()

        self.f0 = f.copy()

        self.dump((self.H, self.r0, self.f0, self.maxstep))

    def determine_step(self, dr, steplengths):

        """Determine step to take according to maxstep

        Normalize all steps as the largest step. This way

        we still move along the eigendirection.

        """

        maxsteplength = np.max(steplengths)

        if maxsteplength >= self.maxstep:

            dr *= self.maxstep / maxsteplength

        return dr

    def update(self, r, f, r0, f0):

        if self.H is None:

            self.H = np.eye(3 * len(self.atoms)) * 70.0

            return

        dr = r - r0

        if np.abs(dr).max() < 1e-7:

            # Same configuration again (maybe a restart):

            return

        df = f - f0

        a = np.dot(dr, df)

        dg = np.dot(self.H, dr)

        b = np.dot(dr, dg)

        self.H -= np.outer(df, df) / a + np.outer(dg, dg) / b

    def replay_trajectory(self, traj):

        """Initialize hessian from old trajectory."""

        if isinstance(traj, str):

            from ase.io.trajectory import PickleTrajectory

            traj = PickleTrajectory(traj, 'r')

        self.H = None

        atoms = traj[0]

        r0 = atoms.get_positions().ravel()

        f0 = atoms.get_forces().ravel()

        for atoms in traj:

            r = atoms.get_positions().ravel()

            f = atoms.get_forces().ravel()

            self.update(r, f, r0, f0)

            r0 = r

            f0 = f

        self.r0 = r0

        self.f0 = f0

class oldBFGS(BFGS):

    def determine_step(self, dr, steplengths):

        """Old BFGS behaviour for scaling step lengths

        This keeps the behaviour of truncating individual steps. Some might

        depend of this as some absurd kind of stimulated annealing to find the

        global minimum.

        """

        dr /= np.maximum(steplengths / self.maxstep, 1.0).reshape(-1, 1)

        return dr