_root_scalar_halley_doc()

Parameters

args : tuple, optional

Extra arguments passed to the objective function and its derivatives.

xtol : float, optional

Tolerance (absolute) for termination.

rtol : float, optional

Tolerance (relative) for termination.

maxiter : int, optional

Maximum number of iterations.

x0 : float, required

Initial guess.

fprime : bool or callable, required

If fprime is a boolean and is True, f is assumed to return the value of derivative along with the objective function. fprime can also be a callable returning the derivative of f. In this case, it must accept the same arguments as f.

fprime2 : bool or callable, required

If fprime2 is a boolean and is True, f is assumed to return the value of 1st and 2nd derivatives along with the objective function. fprime2 can also be a callable returning the 2nd derivative of f. In this case, it must accept the same arguments as f.

options: dict, optional :

Specifies any method-specific options not covered above.

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/_root_scalar.py#395
type: <class 'function'>
Commit: