Adaptive Simulated Annealing: A Near-optimal Connection between Sampling and Counting

Abstract: We present a near-optimal reduction from approximately counting the cardinality of a discrete set to approximately sampling elements of the set. An important application of our work is to approximating the partition function $Z$ of a discrete system, such as the Ising model, matchings or colorings of a graph. The typical approach to estimating the partition function $Z(\beta<sup>*)$ at some desired inverse temperature $\beta<sup>*$ is to define a sequence, which we call a {\em cooling schedule}, $\beta_0=0&lt;\beta_1&lt;...&lt;\beta_\ell=\beta<sup>*$ where Z(0) is trivial to compute and the ratios $Z(\beta_{i+1})/Z(\beta_i)$ are easy to estimate by sampling from the distribution corresponding to $Z(\beta_i)$. Previous approaches required a cooling schedule of length $O<sup>*(\ln{A})$ where $A=Z(0)$, thereby ensuring that each ratio $Z(\beta_{i+1})/Z(\beta_i)$ is bounded. We present a cooling schedule of length $\ell=O<sup>*(\sqrt{\ln{A}})$. For well-studied problems such as estimating the partition function of the Ising model, or approximating the number of colorings or matchings of a graph, our cooling schedule is of length $O<sup>*(\sqrt{n})$, which implies an overall savings of $O<sup>*(n)$ in the running time of the approximate counting algorithm (since roughly $\ell$ samples are needed to estimate each ratio).