Biased Metropolis-Heat-Bath Algorithm for Fundamental-Adjoint SU(2) Lattice Gauge Theory

TL;DR

This paper introduces a new biased Metropolis-heat-bath algorithm for SU(2) lattice gauge theory with fundamental-adjoint action, significantly improving simulation efficiency over traditional methods.

Contribution

The authors develop and demonstrate a novel biased Metropolis-heat-bath algorithm tailored for SU(2) fundamental-adjoint lattice gauge theory, achieving notable efficiency gains.

Findings

Improvement factors of 1.45 to 2.06 over standard Metropolis simulations.

Further optimization yields improvement factors of 1.3 to 1.8.

Enhanced algorithm reduces computational effort in lattice gauge theory simulations.

Abstract

For SU(2) lattice gauge theory with the fundamental-adjoint action an efficient heat-bath algorithm is not known so that one had to rely on Metropolis simulations supplemented by overrelaxation. Implementing a novel biased Metropolis-heat-bath algorithm for this model, we find improvement factors in the range 1.45 to 2.06 over conventionally optimized Metropolis simulations. If one optimizes further with respect to additional overrelaxation sweeps, the improvement factors are found in the range 1.3 to 1.8.