scipy 1.8.0 Pypi GitHub Homepage
Other Docs

The interior-point method uses the primal-dual path following algorithm outlined in . This algorithm supports sparse constraint matrices and is typically faster than the simplex methods, especially for large, sparse problems. Note, however, that the solution returned may be slightly less accurate than those of the simplex methods and will not, in general, correspond with a vertex of the polytope defined by the constraints.

versionadded

Interior-point method for linear programming

Interior-point method for linear programming

The interior-point method uses the primal-dual path following algorithm outlined in . This algorithm supports sparse constraint matrices and is typically faster than the simplex methods, especially for large, sparse problems. Note, however, that the solution returned may be slightly less accurate than those of the simplex methods and will not, in general, correspond with a vertex of the polytope defined by the constraints.

versionadded

References

            <Unimplemented 'footnote' '.. [1] Andersen, Erling D., and Knud D. Andersen. "The MOSEK interior point\n       optimizer for linear programming: an implementation of the\n       homogeneous algorithm." High performance optimization. Springer US,\n       2000. 197-232.'>
           

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


GitHub : /scipy/optimize/_linprog_ip.py#0
type: <class 'module'>
Commit: