scipy 1.8.0 Pypi GitHub Homepage
Other Docs
NotesParametersReturns
solve(self, trust_radius)

Notes

This is algorithm (7.2) of Nocedal and Wright 2nd edition. Only the function that computes the Hessian-vector product is required. The Hessian itself is not required, and the Hessian does not need to be positive semidefinite.

Parameters

trust_radius : float

We are allowed to wander only this far away from the origin.

Returns

p : ndarray

The proposed step.

hits_boundary : bool

True if the proposed step is on the boundary of the trust region.

Solve the subproblem using a conjugate gradient method.

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


GitHub : /scipy/optimize/_trustregion_ncg.py#44
type: <class 'function'>
Commit: