Renormalized Multicanonical Sampling

TL;DR

This paper introduces a renormalized multicanonical sampling method that accelerates the calculation of the density of states for large homogeneous systems by iteratively building from smaller subsystems.

Contribution

It proposes a novel renormalized approach that uses subsystem densities to efficiently approximate the full system's density of states, reducing computational effort.

Findings

Significantly speeds up density of states computation for large systems.

Effective for homogeneous, weakly interacting subsystems.

Reduces the number of iterations needed for convergence.

Abstract

For a homogeneous system divisible into identical, weakly interacting subsystems, the muticanonical procedure can be accelerated if it is first applied to determine of the density of states for a single subsystem. This result is then employed to approximate the state density of a subsystem with twice the size that forms the starting point of a new multicanonical iteration. Since this compound subsystem interacts less on average with its environment, iterating this sequence of steps rapidly generates the state density of the full system.