diff --git a/nifty/minimization/conjugate_gradient.py b/nifty/minimization/conjugate_gradient.py
index e3b71168c70b45c587f98af31833dead1185028e..3470b4744642a74b09288d37f9a39d5223915254 100644
--- a/nifty/minimization/conjugate_gradient.py
+++ b/nifty/minimization/conjugate_gradient.py
@@ -40,7 +40,6 @@ class ConjugateGradient(Minimizer):
----------
Jorge Nocedal & Stephen Wright, "Numerical Optimization", Second Edition,
2006, Springer-Verlag New York
-
"""
def __init__(self, controller, preconditioner=None):
@@ -62,9 +61,7 @@ class ConjugateGradient(Minimizer):
state at last point of the iteration
status : integer
Can be controller.CONVERGED or controller.ERROR
-
"""
-
if preconditioner is None:
preconditioner = self._preconditioner
diff --git a/nifty/minimization/descent_minimizer.py b/nifty/minimization/descent_minimizer.py
index 76f5eb6a4135c23b8d23e20705ca9ac14a063cc0..7e55e674a50478db4e68913edc7f5d00d61db4f8 100644
--- a/nifty/minimization/descent_minimizer.py
+++ b/nifty/minimization/descent_minimizer.py
@@ -18,7 +18,6 @@
from __future__ import division
import abc
-
from .minimizer import Minimizer
from .line_searching import LineSearchStrongWolfe
@@ -38,7 +37,6 @@ class DescentMinimizer(Minimizer):
line_searcher : callable *optional*
Function which infers the step size in the descent direction
(default : LineSearchStrongWolfe()).
-
"""
def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
@@ -68,9 +66,7 @@ class DescentMinimizer(Minimizer):
* the controller returns controller.CONVERGED or controller.ERROR,
* a perfectly flat point is reached,
* according to the line-search the minimum is found,
-
"""
-
f_k_minus_1 = None
controller = self._controller
status = controller.start(energy)
@@ -82,22 +78,17 @@ class DescentMinimizer(Minimizer):
if energy.gradient_norm == 0:
return energy, controller.CONVERGED
- # current position is encoded in energy object
- descent_direction = self.get_descent_direction(energy)
- # compute the step length, which minimizes energy.value along the
- # search direction
+ # compute a step length that reduces energy.value sufficiently
try:
- new_energy = \
- self.line_searcher.perform_line_search(
- energy=energy,
- pk=descent_direction,
- f_k_minus_1=f_k_minus_1)
+ new_energy = self.line_searcher.perform_line_search(
+ energy=energy, pk=self.get_descent_direction(energy),
+ f_k_minus_1=f_k_minus_1)
except RuntimeError:
return energy, controller.ERROR
f_k_minus_1 = energy.value
- # check if new energy value is bigger than old energy value
- if (new_energy.value - energy.value) > 0:
+
+ if new_energy.value > energy.value:
return energy, controller.ERROR
energy = new_energy
diff --git a/nifty/minimization/iteration_controlling/iteration_controller.py b/nifty/minimization/iteration_controlling/iteration_controller.py
index ccdcb3899c77ddd9ba19fdd9afc149ae022bb291..44db3fae259ad4e6c56ff9493972b7980634878a 100644
--- a/nifty/minimization/iteration_controlling/iteration_controller.py
+++ b/nifty/minimization/iteration_controlling/iteration_controller.py
@@ -55,7 +55,6 @@ class IterationController(with_metaclass(NiftyMeta, type('NewBase',
-------
status : integer status, can be CONVERGED, CONTINUE or ERROR
"""
-
raise NotImplementedError
@abc.abstractmethod
@@ -70,5 +69,4 @@ class IterationController(with_metaclass(NiftyMeta, type('NewBase',
-------
status : integer status, can be CONVERGED, CONTINUE or ERROR
"""
-
raise NotImplementedError
diff --git a/nifty/minimization/line_searching/line_search.py b/nifty/minimization/line_searching/line_search.py
index 73199bd450b43e2a6c6a66036189445011778167..04bfd6af961be66d96e2571cd9f98e77726247d2 100644
--- a/nifty/minimization/line_searching/line_search.py
+++ b/nifty/minimization/line_searching/line_search.py
@@ -36,7 +36,6 @@ class LineSearch(with_metaclass(abc.ABCMeta,
LineEnergy object from which we can extract energy at a specific point.
preferred_initial_step_size : float
Initial guess for the step length.
-
"""
def __init__(self):
diff --git a/nifty/minimization/line_searching/line_search_strong_wolfe.py b/nifty/minimization/line_searching/line_search_strong_wolfe.py
index 3658cad4ad076bca13233e4e05fcc67507aa879d..b94da5c8d4b10d5bff9b09a6eee681fcf66bf5ea 100644
--- a/nifty/minimization/line_searching/line_search_strong_wolfe.py
+++ b/nifty/minimization/line_searching/line_search_strong_wolfe.py
@@ -19,7 +19,6 @@
from __future__ import division
from builtins import range
import numpy as np
-
from .line_search import LineSearch
from ...energies import LineEnergy
from ... import dobj
@@ -62,7 +61,6 @@ class LineSearchStrongWolfe(LineSearch):
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,
@@ -99,9 +97,7 @@ class LineSearchStrongWolfe(LineSearch):
-------
energy_star : Energy object
The new Energy object on the new position.
-
"""
-
le_0 = LineEnergy(0., energy, pk, 0.)
# initialize the zero phis
@@ -203,7 +199,6 @@ class LineSearchStrongWolfe(LineSearch):
-------
energy_star : Energy object
The new Energy object on the new position.
-
"""
cubic_delta = 0.2 # cubic interpolant checks
quad_delta = 0.1 # quadratic interpolant checks
@@ -285,7 +280,6 @@ class LineSearchStrongWolfe(LineSearch):
xmin : float
Position of the approximated minimum.
"""
-
with np.errstate(divide='raise', over='raise', invalid='raise'):
try:
C = fpa
diff --git a/nifty/minimization/minimizer.py b/nifty/minimization/minimizer.py
index 0809da8a32d62970a5988e095469a6a2243ae11a..1c6ff8b94fa252368560a7bd5ca7505c6233c365 100644
--- a/nifty/minimization/minimizer.py
+++ b/nifty/minimization/minimizer.py
@@ -22,8 +22,7 @@ from future.utils import with_metaclass
class Minimizer(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
- """ A base class used by all minimizers.
- """
+ """ A base class used by all minimizers."""
@abc.abstractmethod
def __call__(self, energy, preconditioner=None):
@@ -44,5 +43,4 @@ class Minimizer(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
Latest `energy` of the minimization.
status : integer
"""
-
raise NotImplementedError
diff --git a/nifty/minimization/nonlinear_cg.py b/nifty/minimization/nonlinear_cg.py
index 4aedbcc559b8b5921c5d90850a010b89bfb579cc..adba2a0f7927dfc5ae21c994bf4af26f2f0c68f3 100644
--- a/nifty/minimization/nonlinear_cg.py
+++ b/nifty/minimization/nonlinear_cg.py
@@ -34,7 +34,6 @@ class NonlinearCG(Minimizer):
----------
Jorge Nocedal & Stephen Wright, "Numerical Optimization", Second Edition,
2006, Springer-Verlag New York
-
"""
def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
@@ -56,9 +55,7 @@ class NonlinearCG(Minimizer):
state at last point of the iteration
status : integer
Can be controller.CONVERGED or controller.ERROR
-
"""
-
controller = self._controller
status = controller.start(energy)
if status != controller.CONTINUE:
diff --git a/nifty/minimization/relaxed_newton.py b/nifty/minimization/relaxed_newton.py
index 4e9e6ac45194d850483a3e1c5e066600e70c1bf0..77d4649cf46566070b439207b4e9f4c8b294e4d9 100644
--- a/nifty/minimization/relaxed_newton.py
+++ b/nifty/minimization/relaxed_newton.py
@@ -24,7 +24,7 @@ class RelaxedNewton(DescentMinimizer):
def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
super(RelaxedNewton, self).__init__(controller=controller,
line_searcher=line_searcher)
-
+ # FIXME: this does not look idiomatic
self.line_searcher.preferred_initial_step_size = 1.
def get_descent_direction(self, energy):
@@ -46,6 +46,5 @@ class RelaxedNewton(DescentMinimizer):
descent_direction : Field
Returns the descent direction with proposed step length. In a
quadratic potential this corresponds to the optimal step.
-
"""
return -energy.curvature.inverse_times(energy.gradient)
diff --git a/nifty/minimization/steepest_descent.py b/nifty/minimization/steepest_descent.py
index 388dd95d19f38cf99843846906a1de77901c5462..1ffbde24249caacc7db6796a0281858ba45a3522 100644
--- a/nifty/minimization/steepest_descent.py
+++ b/nifty/minimization/steepest_descent.py
@@ -26,7 +26,7 @@ class SteepestDescent(DescentMinimizer):
""" Implementation of the steepest descent minimization scheme.
Also known as 'gradient descent'. This algorithm simply follows the
- functionals gradient for minization.
+ functional's gradient for minization.
Parameters
----------
@@ -37,8 +37,6 @@ class SteepestDescent(DescentMinimizer):
Returns
-------
descent_direction : Field
- Returns the descent direction.
-
+ the descent direction.
"""
-
return -energy.gradient
diff --git a/nifty/minimization/vl_bfgs.py b/nifty/minimization/vl_bfgs.py
index 321281fa06fe98edfe32f88257f19b8c2b6e4eb5..58df5d7815794d2d69fcaa8a68ec6b9ec8b5544b 100644
--- a/nifty/minimization/vl_bfgs.py
+++ b/nifty/minimization/vl_bfgs.py
@@ -20,7 +20,6 @@ from __future__ import division
from builtins import range
from builtins import object
import numpy as np
-
from .descent_minimizer import DescentMinimizer
from .line_searching import LineSearchStrongWolfe
@@ -28,10 +27,8 @@ from .line_searching import LineSearchStrongWolfe
class VL_BFGS(DescentMinimizer):
def __init__(self, controller, line_searcher=LineSearchStrongWolfe(),
max_history_length=5):
-
super(VL_BFGS, self).__init__(controller=controller,
line_searcher=line_searcher)
-
self.max_history_length = max_history_length
def __call__(self, energy):
@@ -117,7 +114,6 @@ class InformationStore(object):
2D circular buffer of scalar products between elements of s and y.
yy : numpy.ndarray
2D circular buffer of scalar products between different elements of y.
-
"""
def __init__(self, max_history_length, x0, gradient):
self.max_history_length = max_history_length
@@ -134,9 +130,7 @@ class InformationStore(object):
@property
def history_length(self):
- """Returns the number of currently stored updates.
-
- """
+ """Returns the number of currently stored updates."""
return min(self.k, self.max_history_length)
@property
@@ -173,7 +167,6 @@ class InformationStore(object):
-------
result : numpy.ndarray
Scalar matrix.
-
"""
m = self.history_length
mmax = self.max_history_length
@@ -216,7 +209,6 @@ class InformationStore(object):
-------
delta : List
List of the new scalar coefficients (deltas).
-
"""
m = self.history_length
b_dot_b = self.b_dot_b
@@ -246,7 +238,6 @@ class InformationStore(object):
Calculates the new position and gradient differences and enters them
into the respective list.
-
"""
mmax = self.max_history_length
self.s[self.k % mmax] = x - self.last_x