Approximate Counting for Spin Systems in Sub-Quadratic Time

Abstract: We present two randomised approximate counting algorithms with $\widetilde{O}(n<sup>{2-c}/\varepsilon<sup>2)$ running time for some constant $c&gt;0$ and accuracy $\varepsilon$: (1) for the hard-core model with fugacity $\lambda$ on graphs with maximum degree $\Delta$ when $\lambda=O(\Delta<sup>{-1.5-c_1})$ where $c_1=c/(2-2c)$; (2) for spin systems with strong spatial mixing (SSM) on planar graphs with quadratic growth, such as $\mathbb{Z}<sup>2$. For the hard-core model, Weitz's algorithm (STOC, 2006) achieves sub-quadratic running time when correlation decays faster than the neighbourhood growth, namely when $\lambda = o(\Delta<sup>{-2})$. Our first algorithm does not require this property and extends the range where sub-quadratic algorithms exist. Our second algorithm appears to be the first to achieve sub-quadratic running time up to the SSM threshold, albeit on a restricted family of graphs. It also extends to (not necessarily planar) graphs with polynomial growth, such as $\mathbb{Z}<sup>d$, but with a running time of the form $$\widetilde{O}\left(n^{2\varepsilon^{{-2}/2^{c(\log}} n)<sup>{1/d}}\right)$$ where $d$ is the exponent of the polynomial growth and $c&gt;0$ is some constant.