+\section{Algorithm Assessment}
+\vspace{-1mm}
+\noindent
+For the assessment of both algorithms we used two different models, the U(1) gauge-Higgs model but couple
+only to one scalar field (see \cite{swa}) and the model presented in this proceedings. In both cases we
+analyzed the bulk observables (and their fluctuations):
+$U_P$ which is the derivative wrt. $\beta$ and $|\phi|^2$ (derivative wrt.
+$\kappa$). First we checked the correctness of the SWA comparing the results for different
+lattices sizes and parameters. Examples for the one flavor model are shown in \cite{swa}.
+Fig.~\ref{obs} shows two observables for the two flavor case.
+$\langle |\phi|^2 \rangle$ (lhs.) and its susceptibility (rhs.) as a function of $\mu$
+for point ``f'' (see phase diagram) on a lattice of size $12^3 \times 60$.
+We observe very good agreement among the different algorithms.
+
+\begin{figure}[h]
+\begin{center}
+\includegraphics[width=\textwidth,clip]{pics/f}
+\includegraphics[width=\textwidth,clip]{pics/f}
+\end{center}
+\vspace{-2mm}
+\caption{Observables $\langle |\phi|^2 \rangle$ (lhs.) and $\chi_\phi$ (rhs.)
+as a function of $\mu$ for point f on a $12^3 \times 60$ lattice size.
+We compare results from the SWA (circles) and the LMA (crosses).} \label{obs}
+\vspace*{-2mm}
+\end{figure}
+
+\noindent
+In order to obtain a measure of the computational effort, we compared the normalized
+autocorrelation time $\overline{\tau}$ as defined in \cite{swa} of the SWA and LMA for
+the one flavored model for different volumes and parameters. We concluded that,
+the SWA outperforms the local update near a phase transition and if
+the acceptance rate of the constrained link variable is not very low (eg. lhs. of Fig.~\ref{auto}).
+On the other hand, when the constrained links have a very low acceptance rate
+the worm algorithm has difficulties to efficiently sample the
+system because it modifies the link occupation number in every move, while the LMA has a sweep with only
+closed surfaces. The plot on the rhs. of Fig.~\ref{auto} shows how $\overline{\tau}$ for
+$U_P$ is larger for the SWA than for the LMA. But this can be overcome by offering
+a sweep of cube updates.
+
+\begin{figure}[t]
+\begin{center}
+\includegraphics[width=\textwidth,clip]{pics/u2}
+\end{center}
+\vspace{-4mm}
+\caption{Normalized autocorrelation times $\overline{\tau}$ for 2 different set
+of parameters. Left: parameters close to a first order phase transition.
+Right: low acceptance rate of the variable $l$. Both simulations correspond
+to a $16^4$ lattice. Data taken from \cite{swa}.} \label{auto}
+\vspace*{-2mm}
+\end{figure}
+
+
+\section{Results}
+\vspace{-1mm}
+\noindent xxxxx
+
+
+\section*{Acknowledgments}
+\vspace{-1mm}
+\noindent
+We thank Hans Gerd Evertz
+for numerous discussions that helped to shape this project and for
+providing us with the software to compute the autocorrelation times.
+This work was supported by the Austrian Science Fund,
+FWF, DK {\it Hadrons in Vacuum, Nuclei, and Stars} (FWF DK W1203-N16)
+and by the Research Executive Agency (REA) of the European Union
+under Grant Agreement number PITN-GA-2009-238353 (ITN STRONGnet).
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