Robust Parameter Selection for Parallel Tempering

Abstract

This paper describes an algorithm for selecting parameter values (e.g. temperature values) at which to measure equilibrium properties with Parallel Tempering Monte Carlo simulation. Simple approaches to choosing parameter values can lead to poor equilibration of the simulation, especially for Ising spin systems that undergo $1^{s}t$-order phase transitions. However, starting from an initial set of parameter values, the careful, iterative respacing of these values based on results with the previous set of values greatly improves equilibration. Example spin systems presented here appear in the context of Quantum Monte Carlo.

Accepted in International Journal of Modern Physics C 2010, http://www.worldscinet.com/ijmpc

References in corpus (5)

• Extended Ensemble Monte Carlo
• Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations
• Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm
• Generalized Ensemble and Tempering Simulations: A Unified View
• The incomplete beta function law for parallel tempering sampling of classical canonical systems

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• borealis - A generalized global update algorithm for Boolean optimization problems
• Investigating the Performance of an Adiabatic Quantum Optimization Processor
• Thermal and quantum fluctuations in extended Kitaev-Yao-Lee spin-orbital model