API Index

WangLandau

This is WangLandau 0.1.0.

CatchupStrategy{B}

Strategy for determining how to update the density of states when a new state is visited for the first time.

This occurs before the density of states is updated with parameter f.

API:

• catchup_enabled(strat::CatchupStrategy)
• catchup_value(strat::CatchupStrategy, sim): only called if

B == true

DosIncrementStrategy

DynamicFractionalCatchup() <: CatchupStrategy

When a new state is visited for the first time, the density of states is set to a fraction of the smallest current non-zero value. The fraction is determined from the current value of \log f.

FixedFractionalCatchup(f) <: CatchupStrategy

When a new state is visited for the first time, the density of states is set to a fixed fraction of the smallest current non-zero value.

FlatHistogramStrategy

FractionOfMean(tol)

Define the histogram flatness criterion to be when the smallest non-zero value is greater than tol times the mean, which is calculated from states that have at least min visits.

NoCatchup() <: CatchupStrategy{false}

The default strategy, does nothing.

ReduceByFactor(; kwargs...)

A DosIncrementStrategy to control parameter $\log f$ by multiplying by a constant factor every time a flat histogram occurs, until a desired minimum value is achieved.

Keyword arguments and their default values are:

• initial = 1.0
• factor = 0.5: must be less than 1
• final = 1e-6

StableNumVisits(min, checksteps)

Define the histogram flatness criterion to be when the number of visited states remains constant for a number of steps checksteps. A state is considered visited if it has been sampled at least minvisits times.

This strategy may be more effective for two-dimensional state spaces (i.e. energy and order parameter), see e.g. Tsai, Wang & Landau, Braz. J. Phys. 38 2008 .

Suggested parameters are min = 2000 and check = N * 10^6 for system size N.

WangLandauProblem(statedefn::D)

This is a simple wrapper for the user-defined statedefn that enables the CommonSolve.jl interface. It can be overloaded so that statedefn does not have to be instantiated directly first.

WangLandauSimulation()

Keyword arguments:

• check_sweeps = 100: The number of sweeps to perform before checking for flatness. A sweep is N steps where N is the size of the system. See system_size.
• max_total_steps = Inf:
• final_logf = 1e-6: when $\log f$ reaches this value the simulation ends.
• logf_strategy = ReduceByFactor(; final = final_logf): Controls how $f$ is updated. Overrides final_logf.
• tol = 0.8: Set tolerance for the flatness of the histogram.
• flat_strategy = FractionOfMean(tol): Define the flatness criterion for the histogram. Overrides tol.
• tasks_per_thread = 4: a multiplier for determining the number of tasks to @spawn. Set to 0 to disable concurrent threads even if more than one thread is running.

CommonSolve.init(problem::WangLandauProblem; kwargs...) -> WangLandauSimulation

Initialise a WangLandauSimulation based on problem.

CommonSolve.solve(::WangLandauProblem)::WangLandauSimulation

CommonSolve.solve!(sim::WangLandauSimulation; kwargs...)

Run WangLandau algorithm on sim.

CommonSolve.step!(sim::WangLandauSimulation, histogram)

Run sim for a single iteration until the histogram is flat.

catchup_enabled(strat)

catchup_value(strat)

commit_trial!(state::S, statedefn::D, trial::T, old_index::I, new_index::I)

Update state by applying the trial move, using information from old_index and new_index, if necessary.

See also random_trial!, revert_trial!.

current_value(strat)

Return the current value of $\log f$ from the strat.

expected_iterations(strat)

final_value(strat)

Return the final value of $\log f$ from the strat.

histogram_index(state::S, statedefn::D, trial::T, old_index::I) -> new_index::I

Calculate the new_index for accessing the density of states, using statedefn, trial or old_index, if necessary.

See also random_trial!.

histogram_size(statedefn::D)

Return a Tuple of integers that specify the size of the histograms. The first is canonically the number of possible energy levels accessible by statedefn.

initialise_state(statedefn::D) -> (state::S, index::I)

Initialise a new state::S for use in a WangLandauSimulation based on the definition statedefn::D provided to WangLandauProblem. D and S are defined by the user and D should be immutable. This function could copy a configuration that is stored in statedefn, but ideally it should reseed a new configuration. Called from CommonSolve.solve! to seed multiple threads.

isconverged(strat)

isflat(strat, hist)::Bool

Checks if histogram hist is flat according to the FlatHistogramStrategy.

random_trial!(state::S, statedefn::D) -> trial::T

Calculate a random trial move for state based on statedefn.

If a trial move cannot be found returns nothing.

See also commit_trial!, revert_trial!.

revert_trial!(state::S, statedefn::D, trial::T, old_index::I, new_index::I)

Returns state to before trial move was performed, using information from old_index and new_index, if necessary.

By default this returns state unaltered. Alternatively, if this method is defined, then it should be sufficient to define amethod for commit_trial! that returns state unaltered.

See also random_trial!, commit_trial!.

system_size(statedefn::D)

Get the canonical size of statedefn, e.g. the number of lattice sites. Used to determine the size of a Monte Carlo sweep.

update!(strat)

update!(strat, sim)

update!(strat, sim)

Optionally update strat with information from the simulation sim. Returns nothing.

wl_trial!(state, old_index, statedefn, logdos, temp_hist, logf, catchup) -> new_index

Obtain a single trial move, compare to current state and commit or reject. Then increment the density of states logdos and histogram temp_hist, with logf and 1, respectively.