diff --git a/demos/wiener_filter_hamiltonian.py b/demos/wiener_filter_hamiltonian.py
index 118bc6e693812f1490483b78eac03775aa7f52e7..80d2721caa75781e3147d3b89694f850ca4fdf1d 100644
--- a/demos/wiener_filter_hamiltonian.py
+++ b/demos/wiener_filter_hamiltonian.py
@@ -78,17 +78,17 @@ if __name__ == "__main__":
D = PropagatorOperator(S=S, N=N, R=R)
def distance_measure(energy, iteration):
- pass
- #print (iteration, ((x-ss).norm()/ss.norm()).real)
+ x = energy.position
+ print (iteration, ((x-ss).norm()/ss.norm()).real)
minimizer = SteepestDescent(convergence_tolerance=0,
iteration_limit=50,
callback=distance_measure)
-# minimizer = VL_BFGS(convergence_tolerance=0,
-# iteration_limit=50,
-# callback=distance_measure,
-# max_history_length=5)
+ minimizer = VL_BFGS(convergence_tolerance=0,
+ iteration_limit=50,
+ callback=distance_measure,
+ max_history_length=5)
m0 = Field(s_space, val=1)
diff --git a/nifty/energies/line_energy.py b/nifty/energies/line_energy.py
index 6268553e98fe321904f16fa790ed5cac879a7e7e..2e82c89b1a29826552a07c82a6d4f8bb16c32615 100644
--- a/nifty/energies/line_energy.py
+++ b/nifty/energies/line_energy.py
@@ -4,19 +4,22 @@ from .energy import Energy
class LineEnergy(Energy):
- def __init__(self, position, energy, line_direction):
- self.energy = energy
- self.line_direction = line_direction
+ def __init__(self, position, energy, line_direction, zero_point=None):
super(LineEnergy, self).__init__(position=position)
+ self.line_direction = line_direction
+
+ if zero_point is None:
+ zero_point = energy.position
+ self._zero_point = zero_point
+
+ position_on_line = self._zero_point + self.position*line_direction
+ self.energy = energy.at(position=position_on_line)
def at(self, position):
- if position == 0:
- return self
- else:
- full_position = self.position + self.line_direction*position
- return self.__class__(full_position,
- self.energy,
- self.line_direction)
+ return self.__class__(position,
+ self.energy,
+ self.line_direction,
+ zero_point=self._zero_point)
@property
def value(self):
diff --git a/nifty/minimization/line_searching/line_search.py b/nifty/minimization/line_searching/line_search.py
index b9d68a6754b278f30fca794784ddcbc325d00ec8..f65b3ea6b0eeb94d591788f7667eefbb3549b5d4 100644
--- a/nifty/minimization/line_searching/line_search.py
+++ b/nifty/minimization/line_searching/line_search.py
@@ -59,6 +59,5 @@ class LineSearch(object, Loggable):
self.f_k_minus_1 = f_k_minus_1
@abc.abstractmethod
- def perform_line_search(self, xk, pk, f_k=None, fprime_k=None,
- f_k_minus_1=None):
+ def perform_line_search(self, energy, pk, f_k_minus_1=None):
raise NotImplementedError
diff --git a/nifty/minimization/line_searching/line_search_strong_wolfe.py b/nifty/minimization/line_searching/line_search_strong_wolfe.py
index ac08d3d18927ec77f0e23caa0a4a1f8a9badaae8..38d268611430c3823eaab81dc402e774501eda63 100644
--- a/nifty/minimization/line_searching/line_search_strong_wolfe.py
+++ b/nifty/minimization/line_searching/line_search_strong_wolfe.py
@@ -54,8 +54,9 @@ class LineSearchStrongWolfe(LineSearch):
# initialize the zero phis
old_phi_0 = self.f_k_minus_1
- phi_0 = self.line_energy.at(0).value
- phiprime_0 = self.line_energy.at(0).gradient
+ energy_0 = self.line_energy.at(0)
+ phi_0 = energy_0.value
+ phiprime_0 = energy_0.gradient
if phiprime_0 == 0:
self.logger.warn("Flat gradient in search direction.")
@@ -82,11 +83,13 @@ class LineSearchStrongWolfe(LineSearch):
self.logger.warn("Increment size became 0.")
alpha_star = 0.
phi_star = phi_0
+ energy_star = energy_0
break
if (phi_alpha1 > phi_0 + c1*alpha1*phiprime_0) or \
((phi_alpha1 >= phi_alpha0) and (i > 1)):
- (alpha_star, phi_star) = self._zoom(alpha0, alpha1,
+ (alpha_star, phi_star, energy_star) = self._zoom(
+ alpha0, alpha1,
phi_0, phiprime_0,
phi_alpha0,
phiprime_alpha0,
@@ -98,10 +101,12 @@ class LineSearchStrongWolfe(LineSearch):
if abs(phiprime_alpha1) <= -c2*phiprime_0:
alpha_star = alpha1
phi_star = phi_alpha1
+ energy_star = energy_alpha1
break
if phiprime_alpha1 >= 0:
- (alpha_star, phi_star) = self._zoom(alpha1, alpha0,
+ (alpha_star, phi_star, energy_star) = self._zoom(
+ alpha1, alpha0,
phi_0, phiprime_0,
phi_alpha1,
phiprime_alpha1,
@@ -119,10 +124,15 @@ class LineSearchStrongWolfe(LineSearch):
# max_iterations was reached
alpha_star = alpha1
phi_star = phi_alpha1
+ energy_star = energy_alpha1
self.logger.error("The line search algorithm did not converge.")
self._last_alpha_star = alpha_star
- return alpha_star, phi_star
+
+ # extract the full energy from the line_energy
+ energy_star = energy_star.energy
+
+ return alpha_star, phi_star, energy_star
def _zoom(self, alpha_lo, alpha_hi, phi_0, phiprime_0,
phi_lo, phiprime_lo, phi_hi, c1, c2):
@@ -176,6 +186,7 @@ class LineSearchStrongWolfe(LineSearch):
if abs(phiprime_alphaj) <= -c2*phiprime_0:
alpha_star = alpha_j
phi_star = phi_alphaj
+ energy_star = energy_alphaj
break
# If not, check the sign of the slope
if phiprime_alphaj*delta_alpha >= 0:
@@ -188,11 +199,12 @@ class LineSearchStrongWolfe(LineSearch):
phiprime_alphaj)
else:
- alpha_star, phi_star = alpha_j, phi_alphaj
+ alpha_star, phi_star, energy_star = \
+ alpha_j, phi_alphaj, energy_alphaj
self.logger.error("The line search algorithm (zoom) did not "
"converge.")
- return alpha_star, phi_star
+ return alpha_star, phi_star, energy_star
def _cubicmin(self, a, fa, fpa, b, fb, c, fc):
"""
diff --git a/nifty/minimization/quasi_newton_minimizer.py b/nifty/minimization/quasi_newton_minimizer.py
index 284a8f135e65a7fa7d44c73d641f767a620b6638..c990da90503f5a5c9688af32cf87af115a4838ae 100644
--- a/nifty/minimization/quasi_newton_minimizer.py
+++ b/nifty/minimization/quasi_newton_minimizer.py
@@ -86,13 +86,11 @@ class QuasiNewtonMinimizer(object, Loggable):
# compute the step length, which minimizes energy.value along the
# search direction
- step_length, step_length = self.line_searcher.perform_line_search(
+ step_length, f_k, new_energy = \
+ self.line_searcher.perform_line_search(
energy=energy,
pk=descend_direction,
f_k_minus_1=f_k_minus_1)
- new_position = current_position + step_length * descend_direction
- new_energy = energy.at(new_position)
-
f_k_minus_1 = energy.value
energy = new_energy
diff --git a/nifty/minimization/vl_bfgs.py b/nifty/minimization/vl_bfgs.py
index 6310b61bbca0c44c91fd932d14a9c9072c0a81e2..1b5e139d7c96243a39d6a675188718b15ac398b3 100644
--- a/nifty/minimization/vl_bfgs.py
+++ b/nifty/minimization/vl_bfgs.py
@@ -20,9 +20,9 @@ class VL_BFGS(QuasiNewtonMinimizer):
self.max_history_length = max_history_length
- def __call__(self, x0, f, fprime, f_args=()):
+ def __call__(self, energy):
self._information_store = None
- return super(VL_BFGS, self).__call__(x0, f, fprime, f_args=())
+ return super(VL_BFGS, self).__call__(energy)
def _get_descend_direction(self, x, gradient):
# initialize the information store if it doesn't already exist