multicanonical_equilibrate

graph_tool.inference.multicanonical_equilibrate(m_state, f_range=(1.0, 1e-06), r=2, flatness=0.95, allow_gaps=True, callback=None, multicanonical_args={}, verbose=False)

Equilibrate a multicanonical Monte Carlo sampling using the Wang-Landau algorithm.

Parameters:

m_stateMulticanonicalState

Initial multicanonical state, where the state density will be stored.

f_rangetuple of two floats (optional, default: (1., 1e-6))

Range of density updates.

rfloat (optional, default: 2.)

Greediness of convergence. At each iteration, the density updates will be reduced by a factor r.

flatnessfloat (optional, default: .95)

Sufficient histogram flatness threshold used to continue the algorithm.

allow_gapsbool (optional, default: True)

If True, gaps in the histogram (regions with zero count) will be ignored when computing the flatness.

callbackfunction (optional, default: None)

If given, this function will be called after each iteration. The function must accept the current state and m_state as arguments.

multicanonical_argsdict (optional, default: {})

Arguments to be passed to state.multicanonical_sweep (e.g. graph_tool.inference.BlockState.multicanonical_sweep()).

verbosebool or tuple (optional, default: False)

If True, progress information will be shown. Optionally, this accepts arguments of the type tuple of the form (level, prefix) where level is a positive integer that specifies the level of detail, and prefix is a string that is prepended to the all output messages.

Returns:

niterint

Number of iterations required for convergence.

References

[wang-efficient-2001]

Fugao Wang, D. P. Landau, “An efficient, multiple range random walk algorithm to calculate the density of states”, Phys. Rev. Lett. 86, 2050 (2001), DOI: 10.1103/PhysRevLett.86.2050 [sci-hub, @tor], arXiv: cond-mat/0011174

[belardinelli-wang-2007]

R. E. Belardinelli, V. D. Pereyra, “Wang-Landau algorithm: A theoretical analysis of the saturation of the error”, J. Chem. Phys. 127, 184105 (2007), DOI: 10.1063/1.2803061 [sci-hub, @tor], arXiv: cond-mat/0702414