Population Annealing for Equilibrium Sampling in Statistical Physics

Population annealing is an algorithm that is well-suited for sampling equilibrium distributions for systems such as spin glasses and configurational glasses with rough free energy landscapes. Population annealing is massively parallel and easily implemented either on large clusters or GPUs. In addition to being massively parallel, population annealing has several attractive features: (1) it gives direct access to thermodynamic potentials, (2) multiple independent runs can be combined using weighted averaging to improve both statistical and systematic errors and (3) equilibration can be assessed with an intrinsic measure. In this talk population annealing will be introduced and put in the context of other sequential Monte Carlo and annealing algorithms. Applications to spin glasses, configurational glasses and first order transitions will be discussed.