https://github.com/carlobaldassi/rrrmc.jl
Reduced-Rejection-Rate Monte Carlo for Ising spin models
https://github.com/carlobaldassi/rrrmc.jl
ising-spin-models julia mcmc monte-carlo
Last synced: 6 months ago
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Reduced-Rejection-Rate Monte Carlo for Ising spin models
- Host: GitHub
- URL: https://github.com/carlobaldassi/rrrmc.jl
- Owner: carlobaldassi
- License: other
- Created: 2016-08-21T03:18:57.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2022-03-16T00:39:44.000Z (over 3 years ago)
- Last Synced: 2025-03-27T08:45:20.323Z (6 months ago)
- Topics: ising-spin-models, julia, mcmc, monte-carlo
- Language: Julia
- Size: 1.05 MB
- Stars: 18
- Watchers: 3
- Forks: 7
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# RRRMC.jl
[![DOCS][docs-img]][docs-url] [![CI][CI-img]][CI-url] [![CODECOV][codecov-img]][codecov-url] [![DOI][zenodo-img]][zenodo-url]
This code implements the Reduced-Rejection-Rate (RRR) Monte Carlo method for Ising spin models described in the paper
*"A method to reduce the rejection rate in Monte Carlo Markov Chains"* by C. Baldassi,
J. Stat. Mech. Theor. Exp., (2017) 3, 033301 [doi:10.1088/1742-5468/aa5335][RRRpaper] ([arXiv][RRRarXiv]).
It also provides:
* a standard Metropolis-Hastings sampler
* a generalized implementation of the BKL method described in the paper
["A new algorithm for Monte Carlo simulation of Ising spin systems"][BKLpaper] by A.B. Bortz, M.H. Kalos and J.L. Lebowitz.
The generalization consists in not requiring that the energy shifts are discrete.
* an implementation of the Waiting time method described in the paper
["Faster Monte Carlo simulations at low temperatures. The waiting time method"][WTMpaper] by J. Dall and P. Sibani.
* an implementation of the "τ-Extremal Optimization" heuristic technique described in the paper
["Optimization with Extremal Dynamics"][EOpaper] by S. Boettcher and A. G. Percus.
The code is written in [Julia]. It requires Julia `1.3` or later.
### Installation
To install the package, use Julia's package manager: from the Julia REPL, type `]` to enter the Pkg REPL mode and run:
```
(v1.3) pkg> add RRRMC
```
Or, equivalently, via the Pkg API:
```
julia> import Pkg; Pkg.add("RRRMC")
```
Dependencies will be installed automatically.
### Documentation
- [**STABLE**][docs-stable-url] — stable version of the documentation
- [**DEV**][docs-dev-url] — *in-development version of the documentation.*
[Julia]: https://julialang.org
[RRRpaper]: https://doi.org/10.1088/1742-5468/aa5335
[RRRarXiv]: http://arxiv.org/abs/1608.05899
[BKLpaper]: https://doi.org/10.1016/0021-9991(75)90060-1
[WTMpaper]: https://doi.org/10.1016/S0010-4655(01)00412-X
[EOpaper]: https://doi.org/10.1103/PhysRevLett.86.5211
[docs-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-url]: https://carlobaldassi.github.io/RRRMC.jl/stable
[CI-img]: https://github.com/carlobaldassi/RRRMC.jl/actions/workflows/ci.yml/badge.svg
[CI-url]: https://github.com/carlobaldassi/RRRMC.jl/actions/workflows/ci.yml
[codecov-img]: https://codecov.io/gh/carlobaldassi/RRRMC.jl/branch/master/graph/badge.svg
[codecov-url]: https://codecov.io/gh/carlobaldassi/RRRMC.jl
[zenodo-img]: https://zenodo.org/badge/66179142.svg
[zenodo-url]: https://zenodo.org/badge/latestdoi/66179142