$\DeclareMathOperator{\arccosh}{arccosh} \DeclareMathOperator*{\argmin}{arg\,min} \DeclareMathOperator{\Exp}{Exp} \newcommand{\geo}[2]{\gamma_{\overset{\frown}{#1,#2}}} \newcommand{\geoS}{\gamma} \newcommand{\geoD}[2]{\gamma_} \newcommand{\geoL}[2]{\gamma(#2; #1)} \newcommand{\gradM}{\nabla_{\M}} \newcommand{\gradMComp}[1]{\nabla_{\M,#1}} \newcommand{\Grid}{\mathcal G} \DeclareMathOperator{\Log}{Log} \newcommand{\M}{\mathcal M} \newcommand{\N}{\mathcal N} \newcommand{\mat}[1]{\mathbf{#1}} \DeclareMathOperator{\prox}{prox} \newcommand{\PT}[3]{\mathrm{PT}_{#1\to#2}#3} \newcommand{\R}{\mathbb R} \newcommand{\SPD}[1]{\mathcal{P}(#1)} \DeclareMathOperator{\Tr}{Tr} \newcommand{\tT}{\mathrm{T}} \newcommand{\vect}[1]{\mathbf{#1}}$

gradient of the distance functions first argument
1
% gradDistance(M,x,y,p) gradient of f(x) = 1/p d^p(x,y)for fixed y in M.


The gradient of $f(x) = \tfrac{1}{p} d^p(x,y)$ for a fixed value $y\in\M$ and $p\geq1$, with standard $p=2$.

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gradient of the second order TV mid point model
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For data $x\colon\Grid\to\M$ on a $m$-dimensional grid $\Grid$ this function returns the gradient $\gradM F$ of the first and second order total variation additive mid point model.

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For data $x\colon\Grid\to\M$ on a $m$-dimensional grid $\Grid$ with $N_{\vect{k}} = \bigl\{\vect{l} : \vect{l}+\vect{e}_j,\ j\in\{1,\ldots,m\}\bigr\}\cap G$ this function comutes the gradient of the TV prior

with respect to all entries of $x$, i.e. $\gradM F\colon\Grid\to T\M$.

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gradient of the second order TV mid point model prior
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For data $x\colon\Grid\to\M$ on a $m$-dimensional grid $\Grid$ this function returns the gradient $\gradM F$ of the absolute second order total variation prior.