Population annealing: A massively parallel algorithm for simulating systems with rough free energy landscapes

Population annealing is a massively parallel sequential Monte Carlo algorithm that is designed to sample equilibrium states of systems with rough free energy landscapes. Population annealing is closely related to simulated annealing. In population annealing, the relevant equilibrium ensemble is represented by a large population of replicas of the system. The population is initialized in an easy to equilibrate region of parameter space and is then "annealed" in parameter space to a more difficult, target region. Resampling the population of replicas during each annealing step ensures that the population remains near equilibrium. The entropy and thermodynamic potentials together with intrinsic estimates of systematic errors are readily accessible from the simulations. We will describe both canonical and microcanonical versions of the population annealing algorithm, and discuss applications to spin glasses, binary hard sphere fluids in the glassy regime, and large q Potts models.