_adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub)

Parameters

x0 : ndarray, shape (n,)

Point at which we wish to estimate derivative.

h : ndarray, shape (n,)

Desired absolute finite difference steps.

num_steps : int

Number of h steps in one direction required to implement finite difference scheme. For example, 2 means that we need to evaluate f(x0 + 2 * h) or f(x0 - 2 * h)

scheme : {'1-sided', '2-sided'}

Whether steps in one or both directions are required. In other words '1-sided' applies to forward and backward schemes, '2-sided' applies to center schemes.

lb : ndarray, shape (n,)

Lower bounds on independent variables.

ub : ndarray, shape (n,)

Upper bounds on independent variables.

Returns

h_adjusted : ndarray, shape (n,)

Adjusted absolute step sizes. Step size decreases only if a sign flip or switching to one-sided scheme doesn't allow to take a full step.

use_one_sided : ndarray of bool, shape (n,)

Whether to switch to one-sided scheme. Informative only for scheme='2-sided' .

Adjust final difference scheme to the presence of bounds.

Examples

See :

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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.

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GitHub : /scipy/optimize/_numdiff.py#11
type: <class 'function'>
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