_line_for_search(x0, alpha, lower_bound, upper_bound)

Parameters

x0 : np.array.

The vector representing the current location. Note np.shape(x0) == (n,) .

alpha : np.array.

The vector representing the direction. Note np.shape(alpha) == (n,) .

lower_bound : np.array.

The lower bounds for each parameter in x0 . If the i``th parameter in ``x0 is unbounded below, then lower_bound[i] should be -np.inf . Note np.shape(lower_bound) == (n,) .

upper_bound : np.array.

The upper bounds for each parameter in x0 . If the i``th parameter in ``x0 is unbounded above, then upper_bound[i] should be np.inf . Note np.shape(upper_bound) == (n,) .

Returns

res : tuple ``(lmin, lmax)``

Given a parameter vector x0 with length n and a direction vector alpha with length n , and lower and upper bounds on each of the n parameters, what are the bounds on a scalar l such that lower_bound <= x0 + alpha * l <= upper_bound .

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/_optimize.py#2831
type: <class 'function'>
Commit: