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ift
NIFTy
Commits
36066654
Commit
36066654
authored
May 09, 2017
by
Matevz, Sraml (sraml)
Browse files
some changes in Line search and started LineSearchStrongWolf
parent
7412dcc3
Changes
3
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nifty/minimization/line_searching/line_search.py
View file @
36066654
...
...
@@ -57,14 +57,13 @@ class LineSearch(Loggable, object):
Parameters
----------
energy : Energy object
Energy object from which we can calculate the
energy, gradient and
curvature at a specific point.
Energy object from which we can calculate the
energy, gradient and
curvature at a specific point.
pk : Field
Unit vector in search direction.
f_k_minus_1 : float *optional*
Value of the field at the k-1 iteration of the line search
procedure.
Unit vector pointing into the search direction.
f_k_minus_1 : float
Value of the fuction (energy) which will be minimized at the k-1
iteration of the line search procedure. (Default: None)
"""
self
.
line_energy
=
LineEnergy
(
position
=
0.
,
...
...
nifty/minimization/line_searching/line_search_strong_wolfe.py
View file @
36066654
...
...
@@ -22,37 +22,49 @@ from .line_search import LineSearch
class
LineSearchStrongWolfe
(
LineSearch
):
"""
Class for finding a step size that satisfies the strong Wolfe conditions.
"""Class for finding a step size that satisfies the strong Wolfe conditions.
Algorithm contains two stages. It begins whit a trial step length and it
keeps increasing the it until it finds an acceptable step length or an
interval. In the second stage the Zoom algorithm is performed which
decreases the size of the interval until an acceptable step length is found.
Parameters
----------
c1 : scalar
Parameter for Armijo condition rule. (Default: 1e-4)
c2 : scalar
Parameter for curvature condition rule. (Default: 0.9)
max_step_size : scalar
Maximum step allowed in to be made in the descent direction.
(Default: 50)
max_iterations : integer
Maximum number of iterations performed by the line search algorithm.
(Default: 10)
max_zoom_iterations : integer
Maximum number of iterations performed by the zoom algorithm.
(Default: 10)
Attributes
----------
c1 : float
Parameter for Armijo condition rule.
c2 : float
Parameter for curvature condition rule.
max_step_size : scalar
Maximum step allowed in to be made in the descent direction.
max_iterations : integer
Maximum number of iterations performed by the line search algorithm.
max_zoom_iterations : integer
Maximum number of iterations performed by the zoom algorithm.
"""
def
__init__
(
self
,
c1
=
1e-4
,
c2
=
0.9
,
max_step_size
=
50
,
max_iterations
=
10
,
max_zoom_iterations
=
10
):
"""
Parameters
----------
f : callable f(x, *args)
Objective function.
fprime : callable f'(x, *args)
Objective functions gradient.
f_args : tuple (optional)
Additional arguments passed to objective function and its
derivation.
c1 : float (optional)
Parameter for Armijo condition rule.
c2 : float (optional)
Parameter for curvature condition rule.
max_step_size : float (optional)
Maximum step size
"""
super
(
LineSearchStrongWolfe
,
self
).
__init__
()
...
...
@@ -63,6 +75,24 @@ class LineSearchStrongWolfe(LineSearch):
self
.
max_zoom_iterations
=
int
(
max_zoom_iterations
)
def
perform_line_search
(
self
,
energy
,
pk
,
f_k_minus_1
=
None
):
"""Performs the first stage of the algorithm.
Its starts with a trial step size and it keeps increasing it until it
satisfy the strong Wolf conditions.
Parameters
----------
energy : Energy object
Energy object from which we will calculate the energy and the
gradient at a specific point.
pk : Field
Unit vector pointing into the search direction.
f_k_minus_1 : float
Value of the function (energy) which will be minimized at the k-1
iteration of the line search procedure. (Default: None)
"""
self
.
_set_line_energy
(
energy
,
pk
,
f_k_minus_1
=
f_k_minus_1
)
c1
=
self
.
c1
c2
=
self
.
c2
...
...
nifty/minimization/quasi_newton_minimizer.py
View file @
36066654
...
...
@@ -104,7 +104,7 @@ class QuasiNewtonMinimizer(Loggable, object):
Returns
-------
x :
f
ield
x :
F
ield
Latest `energy` of the minimization.
convergence : integer
Latest convergence level indicating whether the minimization
...
...
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