We propose a modified power method for computing the subdominant eigenvalue
λ2​ of a matrix or continuous operator. Here we focus on defining
simple Monte Carlo methods for its application. The methods presented use
random walkers of mixed signs to represent the subdominant eigenfuction.
Accordingly, the methods must cancel these signs properly in order to sample
this eigenfunction faithfully. We present a simple procedure to solve this sign
problem and then test our Monte Carlo methods by computing the λ2​ of
various Markov chain transition matrices. We first computed λ2​ for
several one and two dimensional Ising models, which have a discrete phase
space, and compared the relative efficiencies of the Metropolis and heat-bath
algorithms as a function of temperature and applied magnetic field. Next, we
computed λ2​ for a model of an interacting gas trapped by a harmonic
potential, which has a mutidimensional continuous phase space, and studied the
efficiency of the Metropolis algorithm as a function of temperature and the
maximum allowable step size Δ. Based on the λ2​ criterion, we
found for the Ising models that small lattices appear to give an adequate
picture of comparative efficiency and that the heat-bath algorithm is more
efficient than the Metropolis algorithm only at low temperatures where both
algorithms are inefficient. For the harmonic trap problem, we found that the
traditional rule-of-thumb of adjusting Δ so the Metropolis acceptance
rate is around 50% range is often sub-optimal. In general, as a function of
temperature or Δ, λ2​ for this model displayed trends defining
optimal efficiency that the acceptance ratio does not. The cases studied also
suggested that Monte Carlo simulations for a continuum model are likely more
efficient than those for a discretized version of the model.Comment: 23 pages, 8 figure