add new iteration controller

nifty5/__init__.py

@@ -55,7 +55,8 @@ from .probing import probe_with_posterior_samples, probe_diagonal, \
 
 from .minimization.line_search import LineSearch
 from .minimization.iteration_controllers import (
-    IterationController, GradientNormController, DeltaEnergyController)
+    IterationController, GradientNormController, DeltaEnergyController,
+    GradInfNormController)
 from .minimization.minimizer import Minimizer
 from .minimization.conjugate_gradient import ConjugateGradient
 from .minimization.nonlinear_cg import NonlinearCG

nifty5/minimization/iteration_controllers.py

@@ -21,6 +21,7 @@ from __future__ import absolute_import, division, print_function
 from ..compat import *
 from ..logger import logger
 from ..utilities import NiftyMetaBase
+import numpy as np
 
 
 class IterationController(NiftyMetaBase()):
@@ -145,6 +146,48 @@ class GradientNormController(IterationController):
         return self.CONTINUE
 
 
+class GradInfNormController(IterationController):
+    def __init__(self, tol=None, convergence_level=1, iteration_limit=None,
+                 name=None):
+        self._tol = tol
+        self._convergence_level = convergence_level
+        self._iteration_limit = iteration_limit
+        self._name = name
+
+    def start(self, energy):
+        self._itcount = -1
+        self._ccount = 0
+        return self.check(energy)
+
+    def check(self, energy):
+        self._itcount += 1
+
+        crit = energy.gradient.norm(np.inf) / abs(energy.value)
+        if self._tol is not None and crit <= self._tol:
+            self._ccount += 1
+        else:
+            self._ccount = max(0, self._ccount-1)
+
+        # report
+        if self._name is not None:
+            logger.info(
+                "{}: Iteration #{} energy={:.6E} crit={:.2E} clvl={}"
+                .format(self._name, self._itcount, energy.value,
+                        crit, self._ccount))
+
+        # Are we done?
+        if self._iteration_limit is not None:
+            if self._itcount >= self._iteration_limit:
+                logger.warning(
+                    "{} Iteration limit reached. Assuming convergence"
+                    .format("" if self._name is None else self._name+": "))
+                return self.CONVERGED
+        if self._ccount >= self._convergence_level:
+            return self.CONVERGED
+
+        return self.CONTINUE
+
+
 class DeltaEnergyController(IterationController):
     def __init__(self, tol_rel_deltaE, convergence_level=1,
                  iteration_limit=None, name=None):