yango.py 3.53 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
# This program 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.
#
# This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

from __future__ import division
from .minimizer import Minimizer
from .line_search_strong_wolfe import LineSearchStrongWolfe
22
import numpy as np
23
24
25
26


class Yango(Minimizer):
    """ Nonlinear conjugate gradient using curvature
Reimar H Leike's avatar
Reimar H Leike committed
27
28
29
30
31
    The YANGO (Yet Another Nonlinear conjugate Gradient Optimizer)
    uses the curvature to make estimates about suitable descent
    directions. It takes the step that lets it go directly to
    the second order minimum in the subspace spanned by the last
    descent direction and the new gradient.
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58

    Parameters
    ----------
    controller : IterationController
        Object that decides when to terminate the minimization.

    Notes
    -----
    No restarting procedure has been implemented yet.

    References
    ----------
    """

    def __init__(self, controller, line_searcher = LineSearchStrongWolfe(c2=0.1)):
        self._controller = controller
        self._line_searcher = line_searcher

    def __call__(self, energy):
        controller = self._controller
        status = controller.start(energy)
        if status != controller.CONTINUE:
            return energy, status
        f_k_minus_1 = None

        p = -energy.gradient
        A_k = energy.curvature
59
60
61
62
63
        energy, success = self._line_searcher.perform_line_search(     
                energy, p.vdot(p)/(p.vdot(A_k(p)))*p, f_k_minus_1)
        if not success:
            return energy, controller.ERROR
        A_k = energy.curvature
64
        while True:
Reimar H Leike's avatar
Reimar H Leike committed
65
            r = -energy.gradient
66
            f_k = energy.value
67
68
69
70
71
72
            Ar = A_k(r)
            Ap = A_k(p)
            rAr = r.vdot(Ar)
            pAp = p.vdot(Ap)
            pAr = p.vdot(Ar)
            rAp = r.vdot(Ap)
Reimar H Leike's avatar
Reimar H Leike committed
73
74
            rp = r.vdot(p)
            rr = r.vdot(r)
75
76
77
            if rr == 0 or rAr == 0:
                print("gradient norm 0, assuming convergence!")
                return energy, controller.CONVERGED
78
            det = pAp*rAr-np.abs((rAp)*(pAr))
79
            if det < 0:
Reimar H Leike's avatar
Reimar H Leike committed
80
81
                print("negative determinant",det)
                return energy, status
82
83
84
85
86
87
            if det == 0:
                #Try 1D Newton Step
                energy, success = self._line_searcher.perform_line_search(     
                    energy, rr/rAr*r, f_k_minus_1)
            else:
                a = (rAr*rp - rAp*rr)/det
88
                b = (pAp*rr - pAr*rp)/det
89
90
91
                p = a/b*p+r
                energy, success = self._line_searcher.perform_line_search(     
                    energy, p*b, f_k_minus_1)
92
93
            if not success:
                return energy, controller.ERROR
Reimar H Leike's avatar
Reimar H Leike committed
94
            f_k_minus_1 = f_k
95
            status = self._controller.check(energy)
96
97
98
            if status != controller.CONTINUE:
                return energy, status
            A_k = energy.curvature