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Simulated tempering with irreversible Gibbs sampling techniques.

Fahim Faizi1, Pedro J Buigues2, George Deligiannidis3

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|December 9, 2020
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Summary
This summary is machine-generated.

We developed new irreversible algorithms for simulated tempering simulations that improve sampling efficiency. These methods break detailed balance but maintain target distribution invariance, outperforming conventional techniques.

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Area of Science:

  • Computational Physics
  • Statistical Mechanics
  • Chemical Physics

Background:

  • Simulated tempering (ST) is a powerful Monte Carlo method for exploring complex energy landscapes.
  • Conventional ST methods often rely on detailed balance condition (DBC), which can limit efficiency.
  • Breaking DBC while preserving target distribution invariance is a key challenge.

Purpose of the Study:

  • To introduce two novel irreversible algorithms for simulated tempering.
  • To demonstrate improved sampling efficiency compared to conventional methods.
  • To validate the algorithms on diverse systems including Ising models and molecular dynamics.

Main Methods:

  • Developed irreversible algorithms based on Gibbs sampling, specifically targeting temperature swap updates.
  • Broke the detailed balance condition (DBC) while ensuring skewed detailed balance for target distribution invariance.
  • Tested algorithms on a 1D double-well potential, the Ising model, and alanine pentapeptide (ALA5) molecular dynamics simulations.

Main Results:

  • Achieved significant gains in sampling efficiency for the Ising model, evidenced by faster relaxation times for inverse temperature, magnetic susceptibility, and energy density.
  • Observed improved mixing times for inverse temperature and system energy in ALA5 simulations with numerous temperature replicas compared to standard Metropolis-Hastings (MH).
  • Demonstrated favorable constant scaling in Ising spin systems for large numbers of temperature ladders, outperforming both reversible and irreversible MH algorithms.

Conclusions:

  • The novel irreversible algorithms offer a more efficient alternative to conventional simulated tempering methods with DBC, without additional computational overhead.
  • These algorithms show particular promise for large-scale simulations with extensive temperature ranges.
  • Future applications include tailoring these irreversible methods to other dynamical variables for flattening rugged free energy landscapes.