sampled_surface(self, infty_cons_sampl=False)
There are 2 modes, if infty_cons_sampl
is True then the sampled points that are generated outside the feasible domain will be assigned an inf
value in accordance with SHGO rules. This guarantees convergence and usually requires less objective function evaluations at the computational costs of more Delaunay triangulation points.
If infty_cons_sampl
is False, then the infeasible points are discarded and only a subspace of the sampled points are used. This comes at the cost of the loss of guaranteed convergence and usually requires more objective function evaluations.
Sample the function surface.
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