From 32c4df55a3db4d800f684ba8b8bb89a59a10540d Mon Sep 17 00:00:00 2001 From: Martin Reinecke <martin@mpa-garching.mpg.de> Date: Wed, 16 Jan 2019 19:54:06 +0100 Subject: [PATCH] fixes --- docs/source/code.rst | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/docs/source/code.rst b/docs/source/code.rst index 06bc84a55..af6d04aca 100644 --- a/docs/source/code.rst +++ b/docs/source/code.rst @@ -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:`~LinearOperator.target`, whereas adjoint multiplication and inverse multiplication transform from :attr:`~LinearOperator.target` to -:attr:`~LinearOperator.domain`. +: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.RelaxedNewton`, :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 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:`~scipy_minimizer.ScipyCG` and -:class:`L_BFGS_B`. +:class:`~scipy_minimizer.L_BFGS_B`. 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`. +:func:`~ļibrary.wiener_filter_curvature.WienerFilterCurvature`. *FIXME* this link doesn't work. -- GitLab