line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=0.0001, alpha0=1)

Notes

Uses the interpolation algorithm (Armijo backtracking) as suggested by Wright and Nocedal in 'Numerical Optimization', 1999, pp. 56-57

Parameters

f : callable

Function to be minimized.

xk : array_like

Current point.

pk : array_like

Search direction.

gfk : array_like

Gradient of f at point :None:None:`xk`.

old_fval : float

Value of f at point :None:None:`xk`.

args : tuple, optional

Optional arguments.

c1 : float, optional

Value to control stopping criterion.

alpha0 : scalar, optional

Value of alpha at start of the optimization.

Returns

alpha

f_count

f_val_at_alpha

Minimize over alpha, the function f(xk+alpha pk) .

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.optimize._linesearch.line_search_BFGS

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/_linesearch.py#607
type: <class 'function'>
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