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Rare-event sampling: occupation-based performance measures for parallel tempering and infinite swapping Monte Carlo

J D Doll¹, Nuria Plattner, David L Freeman

¹Department of Chemistry, Brown University, Providence, Rhode Island 02912, USA.

The Journal of Chemical Physics

|December 5, 2012

Summary

This summary is machine-generated.

This study introduces new performance measures for tempering-based Monte Carlo sampling methods. These measures show partial and infinite swapping outperform parallel tempering for rare-event sampling in simulations.

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Area of Science:

Computational chemistry and physics
Statistical mechanics
Rare-event simulation

Background:

Tempering-based Monte Carlo methods are crucial for sampling complex systems.
Assessing the efficiency of these methods, particularly for rare events, is challenging.
Existing performance metrics may not fully capture the nuances of different tempering strategies.

Purpose of the Study:

To identify a fundamental property of tempering-based Monte Carlo methods.
To develop broadly applicable and easy-to-implement performance measures.
To compare the performance of parallel tempering, partial swapping, and infinite swapping for rare-event sampling.

Main Methods:

Identification of a rigorous property common to parallel tempering, partial swapping, and infinite swapping.
Development of novel performance metrics based on this property.
Application of these metrics to Lennard-Jones cluster simulations for equilibrium properties.

Main Results:

A rigorous property applicable to various tempering methods was identified.
New performance measures were developed and demonstrated to be informative and easy to implement.
Partial and infinite swapping demonstrated superior performance compared to parallel tempering in Lennard-Jones cluster simulations.

Conclusions:

The developed performance measures offer a robust way to evaluate rare-event sampling methods.
Partial and infinite swapping present a more efficient alternative to parallel tempering for specific applications.
This work provides valuable tools for optimizing computational strategies in statistical mechanics.