Math 2.0

Statistical mechanics, structure of matter Definition

Parallel tempering

Builds on Markov chain Monte CarloBoltzmann distributionDetailed balanceMetropolis–Hastings algorithm

Definition

Let SS be a finite set of configurations with energy H ⁣:S→ℝH \colon S \to \R, and let πβ(x)∝e−βH(x)\pi_\beta(x) \propto e^{-\beta H(x)} be the Boltzmann distribution at inverse temperature β\beta. Fix inverse temperatures 0<β1<β2<⋯<βM0 < \beta_1 < \beta_2 < \dots < \beta_M. Parallel tempering is the Markov chain on SMS^M, with states (x1,…,xM)(x_1, \dots, x_M), whose steps are of two kinds.

  1. Local moves: for each ii, update xix_i by a Markov chain that leaves πβi\pi_{\beta_i} invariant.
  2. Swap moves: choose i∈{1,…,M−1}i \in \set{1, \dots, M - 1} and propose to exchange xix_i and xi+1x_{i+1}; accept the exchange with probability min⁡(1,exp⁡((βi−βi+1)(H(xi)−H(xi+1)))).\min\Big(1, \exp\big((\beta_i - \beta_{i+1}) (H(x_i) - H(x_{i+1}))\big)\Big).

Remarks

Both kinds of moves leave the product measure πβ1⊗⋯⊗πβM\pi_{\beta_1} \otimes \dots \otimes \pi_{\beta_M} invariant. For the swap, the ratio of the product weights after and before is as follows. πβi(xi+1)πβi+1(xi)πβi(xi)πβi+1(xi+1)=e(βi−βi+1)(H(xi)−H(xi+1)),\frac{\pi_{\beta_i}(x_{i+1}) \pi_{\beta_{i+1}}(x_i)}{\pi_{\beta_i}(x_i) \pi_{\beta_{i+1}}(x_{i+1})} = e^{(\beta_i - \beta_{i+1})(H(x_i) - H(x_{i+1}))}, so the rule of clause 2 is the Metropolis rule for a symmetric proposal, and it satisfies detailed balance. Hence the marginal of xMx_M is πβM\pi_{\beta_M}, and averages of xMx_M estimate thermal averages at the lowest temperature. Example: let the hotter copy xix_i have the lower energy. Then βi−βi+1<0\beta_i - \beta_{i+1} < 0 and H(xi)−H(xi+1)<0H(x_i) - H(x_{i+1}) < 0, so the exponent is positive and the swap is always accepted. In this way low-energy states move towards the cold end. Non-example: accepting every swap would break the invariance, because a high-energy state from the hot copy would be pushed into the cold copy too often. The temperatures must be close enough that the energy distributions of neighbouring copies overlap; otherwise almost all swaps are rejected.

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  1. Boltzmann distribution concept
  2. Detailed balance concept
  3. Markov chain Monte Carlo concept
  4. Metropolis–Hastings algorithm concept
  5. Parallel tempering definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Parallel_tempering
About this page
TagHB7J6SYG00
URIm2://HB7J6SYG00
Kinddefinition
WritingW0 stub
MSC82M31, 65C05, 65C40
DefinesParallel tempering
UsesMarkov chain Monte Carlo, Boltzmann distribution, Detailed balance, Metropolis–Hastings algorithm
Changed3 November 2026

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