more docs

docs/source/code.rst

@@ -288,6 +288,11 @@ Of these algorithms, only :class:`RelaxedNewton` requires the energy object to
 provide a :attr:`~Energy.curvature` property, the others only need energy
 values and gradients.
 
+The flexibility of NIFTy's design allows using externally provided
+minimizers. With only small effort, adapters for two SciPy minimizers were
+written; they are available under the names :class:`NewtonCG` and
+:class:`L_BFGS_B`.
+
 
 Application to operator inversion
 ---------------------------------
@@ -302,5 +307,5 @@ A classical example is the information propagator
 which must be applied when calculating a Wiener filter. Only its inverse
 application is straightforward; to use it in forward direction, we make use
 of NIFTy's :class:`InversionEnabler` class, which internally performs a
-minimization of a :class:`QuadraticEnergy` by means of a
+minimization of a :class:`QuadraticEnergy` by means of the
 :class:`ConjugateGradient` algorithm.

nifty4/minimization/__init__.py

@@ -9,7 +9,7 @@ from .descent_minimizer import DescentMinimizer
 from .steepest_descent import SteepestDescent
 from .vl_bfgs import VL_BFGS
 from .relaxed_newton import RelaxedNewton
-from .scipy_minimizer import NewtonCG, L_BFGS_B
+from .scipy_minimizer import ScipyMinimizer, NewtonCG, L_BFGS_B
 from .energy import Energy
 from .quadratic_energy import QuadraticEnergy
 from .line_energy import LineEnergy
@@ -17,5 +17,5 @@ from .line_energy import LineEnergy
 __all__ = ["LineSearch", "LineSearchStrongWolfe", "IterationController",
            "GradientNormController",
            "Minimizer", "ConjugateGradient", "NonlinearCG", "DescentMinimizer",
-           "SteepestDescent", "VL_BFGS", "RelaxedNewton", "NewtonCG",
-           "L_BFGS_B", "Energy", "QuadraticEnergy", "LineEnergy"]
+           "SteepestDescent", "VL_BFGS", "RelaxedNewton", "ScipyMinimizer",
+           "NewtonCG", "L_BFGS_B", "Energy", "QuadraticEnergy", "LineEnergy"]

nifty4/minimization/scipy_minimizer.py

@@ -104,11 +104,23 @@ class ScipyMinimizer(Minimizer):
 
 
 def NewtonCG(controller):
+    """Returns a ScipyMinimizer object carrying out the Newton-CG algorithm.
+
+    See Also
+    --------
+    ScipyMinimizer
+    """
     return ScipyMinimizer(controller, "Newton-CG",
                           {"xtol": 1e-20, "maxiter": None}, True)
 
 
 def L_BFGS_B(controller, maxcor=10):
+    """Returns a ScipyMinimizer object carrying out the L-BFGS-B algorithm.
+
+    See Also
+    --------
+    ScipyMinimizer
+    """
     return ScipyMinimizer(controller, "L-BFGS-B",
                           {"ftol": 1e-20, "gtol": 1e-20, "maxcor": maxcor},
                           False)