Scipy_DualAnnealing

class sherpa.optmethods.optscipy.Scipy_DualAnnealing(name: str | None = None, **kwargs)[source] [edit on github]

Bases: ScipyBase

Optimizer using scipy.optimize.dual_annealing.

See the scipy.optimize.dual_annealing documentation for details of all parameters. Sherpa will automatically convert statistics functions, input values, parameter limits etc. to the format required by the scipy function. The following attributes can be set as attributes of this class.

maxiter

The maximum number of iterations.

Type:: int

minimizer_kwargs

A dictionary of options to pass to the local minimizer.

Type:: dict

initial_temp

A higher value allows dual_annealing to escape local minima that it is trapped in.

Type:: float

restart_temp_ratio

During the annealing process, temperature is decreasing, when it reaches initial_temp * restart_temp_ratio, the reannealing process is triggered. Default value of the ratio is 2e-5. Range is (0, 1).

Type:: float

visit

Parameter for visiting distribution. Default value is 2.62. Higher values allow jumps to more distant regions. The value range is (1, 3].

Type:: float

accept

Control for the acceptance probability. Default value is -5.0 with a range (-1e4, -5].

Type:: float

maxfun

Soft limit for the number of objective function calls. Default value is 1e7.

Type:: int

rng

Random number generator instance or seed.

Type:: {None, int, numpy.random.Generator}

no_local_search

If True, the local search is not performed.

Type:: bool

callback

A callable called after each iteration.

Type:: callable

Attributes Summary

default_config

The default settings for the optimiser.

Methods Summary

fit(statfunc, pars, parmins, parmaxes[, ...])

Run the optimiser.

Attributes Documentation

default_config: The default settings for the optimiser.

Methods Documentation

fit(statfunc: Callable[[Sequence[float] | ndarray], tuple[float, ndarray]], pars: Sequence[float] | ndarray, parmins: Sequence[float] | ndarray, parmaxes: Sequence[float] | ndarray, statargs: Any | None = None, statkwargs: Any | None = None) → tuple[bool, ndarray, float, str, dict[str, Any]] [edit on github]

Run the optimiser.

Changed in version 4.18.0: The statargs and statkwargs arguments are now ignored.

Changed in version 4.16.0: The statkwargs argument now defaults to None rather than {}.

Parameters:

statfunc (function) – Given a list of parameter values as the first argument and, as the remaining positional arguments, statargs and statkwargs as keyword arguments, return the statistic value.
pars (sequence) – The start position of the model parameter values.
parmins (sequence) – The minimum allowed values for each model parameter. This must match the length of pars.
parmaxes (sequence) – The maximum allowed values for each model parameter. This must match the length of pars.
statargs (optional) – This is currently unused.
statkwargs (dict, optional) – This is currently unused.

Returns:

newpars – The tuple contains: boolean indicating whether the optimization succeeded or not, the best fit parameters as a NumPy array, the statistic value at the best-fit location, a string message indicating the status, and a dictionary containing information about the optimisation (this depends on the optimiser).

Return type:

tuple