Parameters

acceptance_param : float

Parameter for acceptance distribution. It is used to control the probability of acceptance. The lower the acceptance parameter, the smaller the probability of acceptance. Default value is -5.0 with a range (-1e4, -5].

visit_dist : VisitingDistribution

Instance of VisitingDistribution class.

func_wrapper : ObjectiveFunWrapper

Instance of ObjectiveFunWrapper class.

minimizer_wrapper: LocalSearchWrapper :

Instance of LocalSearchWrapper class.

rand_gen : {None, int, `numpy.random.Generator`,

numpy.random.RandomState }, optional

If seed is None (or :None:None:`np.random`), the numpy.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed . If seed is already a Generator or RandomState instance then that instance is used.

energy_state: EnergyState :

Instance of EnergyState class.

Class that implements within a Markov chain the strategy for location acceptance and local search decision making.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /scipy/optimize/_dual_annealing.py#214
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