\documentclass[fleqn,aspectratio=169]{beamer} \setbeamerfont{frametitle}{family=\bf} \setbeamerfont{normal text}{family=\rm} \setbeamertemplate{navigation symbols}{} \usepackage{textcomp,rotating} \title{Rejection-free cluster Monte Carlo in arbitrary external fields} \subtitle{Phys Rev E \textbf{98}, 063306 (2018)} \author{Jaron Kent-Dobias \and James P Sethna} \institute{Cornell University} \date{} \begin{document} \def\tr{\mathop{\mathrm{Tr}}\nolimits} \begin{frame} \maketitle \end{frame} \begin{frame} \frametitle{Simulating critical lattice models is slow} \begin{columns} \begin{column}{0.5\textwidth} \alert<2>{Critical timescales diverge like $L^z$.} \vspace{1em} \alert<3-4>{2D Ising local algorithms have $z\simeq2$--4.} \vspace{1em} \alert<5-9>{Cluster methods have $z\simeq0.3$!} \vspace{1em} \alert<10-11>{Don't naturally work with on-site potentials like external fields.} \vspace{1em} \alert<12>{Our extension admits arbitrary on-site potentials for most lattice models.} \end{column} \begin{column}{0.5\textwidth} \begin{overprint} \onslide<1-3>\includegraphics[width=\columnwidth]{figs/ising_hl_00_0} \onslide<4-5,7>\includegraphics[width=\columnwidth]{figs/ising_hl_00_1} \onslide<8,11->\includegraphics[width=\columnwidth]{figs/ising_hl_00_2} \onslide<6,9-10>\includegraphics[width=\columnwidth]{figs/ising_hl_00_3} \end{overprint} \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{Lattice models} Consider spins $s\in X$ with symmetry group $G$ and \[ \mathcal H=-\sum_{\langle ij\rangle}J(s_i,s_j)-\sum_iB(s_i) \] Cluster method for $B=0$ if self-inverse $r^{-1}=r\in G$ are transitive. \vspace{2em} \small \begin{tabular}{l|ccl} \hline & spin space ($X$) & symmetry group ($G$) & self-inverse elements ($r$'s) \\ \hline \alert<2>{Ising} & \alert<2>{$\pm1$} & \alert<2>{$\mathbb Z_2$} & \alert<2>{spin flips} \\ \alert<3>{$n$-vector} & \alert<3>{$(n-1)$ sphere} & \alert<3>{$\mathrm O(n)$} & \alert<3>{reflections through origin} \\ Potts & $\{1,\ldots,q\}$ & Symmetric ($S_q$)& transpositions \\ Clock & regular $q$-gon vertices & Dihedral ($D_q$) & reflections through origin\\ Roughening & $\mathbb Z$ & Infinite Dihedral ($D_\infty$)& subtraction by integer\\ \alert<4>{Chiral Potts} & \alert<4>{$\{1,\ldots,q\}$} & \alert<4>{$\mathbb Z/q\mathbb Z$} & \alert<4>{none} \end{tabular} \end{frame} \begin{frame} \frametitle{Cluster methods without potentials} \begin{columns} \begin{column}{0.5\textwidth} With $\Delta\mathcal H_{ij}=J(r\cdot s_i, s_j)-J(s_i, s_j)$ and \[ p_{ij}=\begin{cases}1-e^{\beta\Delta\mathcal H_{ij}} & \Delta\mathcal H_{ij}>0 \\ 0 & \text{otherwise,}\end{cases} \] \begin{enumerate} \item \alert<2>{Pick self-inverse $r\in G$.} \item \alert<3>{Pick a random site, add to cluster.} \item \alert<4-7>{Add neighbors to cluster with probability $p_{ij}$.} \item \alert<8-9>{Repeat for all sites added to cluster.} \item \alert<10>{Apply $r$ to cluster.} \end{enumerate} \end{column} \begin{column}{0.5\textwidth} \begin{overprint} \onslide<1-2>\includegraphics[width=\columnwidth]{figs/ising_hl_0_1} \onslide<3>\includegraphics[width=\columnwidth]{figs/ising_hl_0_2} \onslide<4>\includegraphics[width=\columnwidth]{figs/ising_hl_0_3} \onslide<5>\includegraphics[width=\columnwidth]{figs/ising_hl_0_4} \onslide<6>\includegraphics[width=\columnwidth]{figs/ising_hl_0_5} \onslide<7>\includegraphics[width=\columnwidth]{figs/ising_hl_0_6} \onslide<8>\includegraphics[width=\columnwidth]{figs/ising_hl_0_7} \onslide<9>\includegraphics[width=\columnwidth]{figs/ising_hl_0_8} \onslide<10>\includegraphics[width=\columnwidth]{figs/ising_hl_0_9} \end{overprint} \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{The ghost site representation} \begin{columns} \begin{column}{0.5\textwidth} \includegraphics[width=\textwidth]{figs/ghost_site} \vspace{1em} \tiny{Coniglio, de Liberto, Monroy, \& Peruggi. J Phys A \textbf{22} (1989) L837} \end{column} \begin{column}{0.5\textwidth} \alert<2>{Introduce new site $0$ adjacent to all others.} \vspace{1em} \alert<3>{Draw object $s_0\in G$ on site from symmetry group $G$, \emph{not} spin space $X$.} \vspace{1em} \alert<4>{Take the new Hamiltonian \[ \begin{aligned} \tilde{\mathcal H} &=-\sum_{\langle ij\rangle}J(s_i,s_j)-\sum_iB(s_0^{-1}\cdot s_i) \\ &=-\sum_{\langle ij\rangle'}\tilde J(s_i,s_j) \end{aligned} \] for $\tilde J(s_0,s_i)=B(s_0^{-1}\cdot s_i)$.} \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{Cluster methods with potentials} \framesubtitle{Ising model with uniform $B(s)=Hs$} \begin{columns} \begin{column}{0.5\textwidth} With $\Delta\tilde{\mathcal H}_{ij}=\tilde J(r\cdot s_i,s_j)-\tilde J(s_i,s_j)$ and \[ \tilde p_{ij}=\begin{cases}1-e^{\beta\Delta\tilde{\mathcal H}_{ij}} & \Delta\tilde{\mathcal H}_{ij}>0 \\ 0 & \text{otherwise,}\end{cases} \] \begin{enumerate} \item \alert<2>{Pick self-inverse $r\in G$.} \item \alert<3>{Pick a random site, add to cluster.} \item \alert<4-8>{Add neighbors to cluster with probability $\tilde p_{ij}$.} \item \alert<9-10>{Repeat for all sites added to cluster.} \item \alert<11-12>{Apply $r$ to cluster.} \end{enumerate} \end{column} \begin{column}{0.5\textwidth} \begin{overprint} \onslide<1-2>\includegraphics[width=\columnwidth]{figs/ising_hl_h_1} \onslide<3>\includegraphics[width=\columnwidth]{figs/ising_hl_h_2} \onslide<4>\includegraphics[width=\columnwidth]{figs/ising_hl_h_3} \onslide<5>\includegraphics[width=\columnwidth]{figs/ising_hl_h_4} \onslide<6>\includegraphics[width=\columnwidth]{figs/ising_hl_h_5} \onslide<7>\includegraphics[width=\columnwidth]{figs/ising_hl_h_6} \onslide<8>\includegraphics[width=\columnwidth]{figs/ising_hl_h_7} \onslide<9>\includegraphics[width=\columnwidth]{figs/ising_hl_h_8} \onslide<10>\includegraphics[width=\columnwidth]{figs/ising_hl_h_9} \onslide<11>\includegraphics[width=\columnwidth]{figs/ising_hl_h_10} \onslide<12>\includegraphics[width=\columnwidth]{figs/ising_hl_h_11} \end{overprint} \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{Cluster methods with potentials} \framesubtitle{XY model with $B(s)=h_5\cos(5\theta)$} \begin{columns} \begin{column}{0.5\textwidth} With $\Delta\tilde{\mathcal H}_{ij}=\tilde J(r\cdot s_i,s_j)-\tilde J(s_i,s_j)$ and \[ \tilde p_{ij}=\begin{cases}1-e^{\beta\Delta\tilde{\mathcal H}_{ij}} & \Delta\tilde{\mathcal H}_{ij}>0 \\ 0 & \text{otherwise,}\end{cases} \] \begin{enumerate} \item \alert<2>{Pick self-inverse $r\in G$.} \item \alert<3>{Pick a random site, add to cluster.} \item \alert<3>{Add neighbors to cluster with probability $\tilde p_{ij}$.} \item \alert<3>{Repeat for all sites added to cluster.} \item \alert<4-6>{Apply $r$ to cluster.} \end{enumerate} \end{column} \begin{column}{0.5\textwidth} \begin{overprint} \onslide<1>\includegraphics[width=\columnwidth]{figs/vector_hl_h5_0} \onslide<2>\includegraphics[width=\columnwidth]{figs/vector_hl_h5_1} \onslide<3>\includegraphics[width=\columnwidth]{figs/vector_hl_h5_2} \onslide<4>\includegraphics[width=\columnwidth]{figs/vector_hl_h5_3} \onslide<5>\includegraphics[width=\columnwidth]{figs/vector_hl_h5_4} \onslide<6>\includegraphics[width=\columnwidth]{figs/vector_hl_h5_5} \end{overprint} \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{Cluster methods with potentials} \framesubtitle{XY model with $B(s)=h_n\cos(n\theta)$} \begin{columns} \begin{column}{0.38\textwidth} \includegraphics[width=\columnwidth]{figs/harmonic_susceptibilities} \end{column} \begin{column}{0.62\textwidth} Symmetry breaking fields $B(\theta)=h_n\cos(n\theta)$ expected due to lattice anisotropies. \vspace{1em} Jos\'e, Kadanoff, Kirkpatrick \& Nelson (1977) predict relevance for $n\leq4$. \vspace{1em} Ala-Nissila et al.\ (1994) used hybrid metropolis and Wolff with cluster rejection to study. \vspace{1em} New method reveals different phenomena for $n=4,6$ faster and without rejection. \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{Is it efficient?} \begin{columns} \begin{column}{0.6\textwidth} Yes! Extension is fast and natural. \vspace{1.5em} Dynamic scaling works in entire $t$--$h$ plane for every model we've looked at. \vspace{1.5em} \alert<2>{Universal scaling functions decay with predictable power law $h^{-z\nu/\beta\delta}$ with Wolff or Swendsen--Wang $z$.} \vspace{1.5em} Distribution self-inverse $r\in G$ are sampled from affects performance far from criticality. \end{column} \begin{column}{0.4\textwidth} \begin{overprint} \onslide<1,3->\includegraphics[width=\textwidth]{figs/times_3dising_new} \onslide<2>\includegraphics[width=\textwidth]{figs/times_3dising_new_alert} \end{overprint} \begin{overprint} \onslide<1,3->\includegraphics[width=\textwidth]{figs/times_3dxy_new} \onslide<2>\includegraphics[width=\textwidth]{figs/times_3dxy_new_alert} \end{overprint} \end{column} \end{columns} \end{frame} \begin{frame} \frametitle{Summary} Generic and fast extension to cluster Monte Carlo with arbitrary on-site potentials. \vspace{1em} Demonstrated efficient for canonical fields, symmetry-breaking potentials. \vspace{1em} Using now with spins on sites and bonds that act as effective fields for each other. \vspace{1em} Developing a generic way to optimize distributions self-inverse $r\in G$ are drawn from. \vspace{1em} Phys Rev E \textbf{98}, 063306 (2018) or contact Jaron Kent-Dobias (\texttt{jpk247@cornell.edu}). \vspace{2em} \centering \Large Questions? \end{frame} \end{document}