approx_jacobian(x, func, epsilon, *args)

Notes

The approximation is done using forward differences.

Parameters

x : array_like

The state vector at which to compute the Jacobian matrix.

func : callable f(x,*args)

The vector-valued function.

epsilon : float

The perturbation used to determine the partial derivatives.

args : sequence

Additional arguments passed to func.

Returns

An array of dimensions ``(lenf, lenx)`` where ``lenf`` is the length

of the outputs of `func`, and ``lenx`` is the number of elements in

`x`.

Approximate the Jacobian matrix of a callable function.

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/_slsqp_py.py#34
type: <class 'function'>
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