Amoeba Monte Carlo algorithms for random trees with controlled branching activity: Efficient trial move generation and universal dynamics

The reptation Monte Carlo algorithm is a simple, physically motivated and efficient method for equilibrating semidilute solutions of linear polymers. Here, we propose two simple generalizations for the analog Amoeba algorithm for randomly branching chains, which allow us to efficiently deal with random trees with controlled branching activity. We analyze the rich relaxation dynamics of Amoeba algorithms and demonstrate the existence of an unexpected scaling regime for the tree relaxation. Our results suggest that the equilibration time for Amoeba algorithms scales in general like N^{2}〈n_{lin}〉^{Δ}, where N denotes the number of tree nodes, 〈n_{lin}〉 the mean number of linear segments the trees are composed of, and Δ≃0.4.