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Parameters
initialize(self, n, approx_type)

Allocate internal memory for storing and updating the Hessian or its inverse.

Parameters

n : int

Problem dimension.

approx_type : {'hess', 'inv_hess'}

Selects either the Hessian or the inverse Hessian. When set to 'hess' the Hessian will be stored and updated. When set to 'inv_hess' its inverse will be used instead.

Initialize internal matrix.

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/_hessian_update_strategy.py#120
type: <class 'function'>
Commit: