Commit 32c4df55 authored by Martin Reinecke's avatar Martin Reinecke


parent 10e069e8
......@@ -270,7 +270,7 @@ Direct multiplication and adjoint inverse multiplication transform a field
defined on the operator's :attr:`~LinearOperator.domain` to one defined on the
operator's :attr:``, whereas adjoint multiplication and
inverse multiplication transform from :attr:`` to
:attr:`~LinearOperator.domain`. *FIXME* links don't work
.. currentmodule:: nifty5.operators
......@@ -379,7 +379,7 @@ Minimization algorithms
All minimization algorithms in NIFTy inherit from the abstract
:class:`~minimizer.Minimizer` class, which presents a minimalistic interface
consisting only of a :meth:`~minimizer.Minimizer.__call__()` method taking an
consisting only of a :meth:`~minimizer.Minimizer.__call__()` *FIXME* method taking an
:class:`~energy.Energy` object and optionally a preconditioning operator, and
returning the energy at the discovered minimum and a status code.
......@@ -399,17 +399,16 @@ Many minimizers for nonlinear problems can be characterized as
This family of algorithms is encapsulated in NIFTy's
:class:`~descent_minimizers.DescentMinimizer` class, which currently has three
concrete implementations: :class:`~descent_minimizers.SteepestDescent`,
:class:`~descent_minimizers.NewtonCG`, :class:`~descent_minimizers.L_BFGS` and
:class:`~descent_minimizers.VL_BFGS`. Of these algorithms, only
:class:`~descent_minimizers.RelaxedNewton` requires the energy object to provide
:class:`~descent_minimizers.NewtonCG` requires the energy object to provide
a :attr:`~energy.Energy.metric` property, the others only need energy values and
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:`~scipy_minimizer.ScipyCG` and
Application to operator inversion
......@@ -432,4 +431,4 @@ performs a minimization of a
:class:`~minimization.quadratic_energy.QuadraticEnergy` by means of the
:class:`~minimization.conjugate_gradient.ConjugateGradient` algorithm. An
example is provided in
:func:`~ļibrary.wiener_filter_curvature.WienerFilterCurvature`. *FIXME* this link doesn't work.
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