Computation within the auxiliary field approach ?
Recently, the classical auxiliary field methodology has been developed as a new simulation technique for performing calculations within the framework of classical statistical mechanics. Since the approach suffers from a sign problem, a judicious choice of the sampling algorithm, allowing a fast statistical convergence and an efficient generation of field configurations, is of fundamental importance for a successful simulation. In this paper we focus on the computational aspects of this simulation methodology. We introduce two different types of algorithms, the single-move auxiliary field Metropolis Monte Carlo algorithm and two new classes of force-based algorithms, which enable multiple-move propagation. In addition, to further optimize the sampling, we describe a preconditioning scheme, which permits to treat each field degree of freedom individually with regard to the evolution through the auxiliary field configuration space. Finally, we demonstrate the validity and assess the competitiveness of these algorithms on a representative practical example. We believe that they may also provide an interesting possibility for enhancing the computational efficiency of other auxiliary field methodologies.
computational methods numerical methods monte carlo methods analysis of algorithms and problem complexity classical equilibrium statistical mechanics