_check_sparse_inputs(options, meth, A_ub, A_eq)

Parameters

A_ub : 2-D array, optional

2-D array such that A_ub @ x gives the values of the upper-bound inequality constraints at x .

A_eq : 2-D array, optional

2-D array such that A_eq @ x gives the values of the equality constraints at x .

options : dict

A dictionary of solver options. All methods accept the following generic options:

maxiter

maxiter

disp

disp

For method-specific options, see show_options('linprog') .

method : str, optional

The algorithm used to solve the standard form problem.

Returns

A_ub : 2-D array, optional

2-D array such that A_ub @ x gives the values of the upper-bound inequality constraints at x .

A_eq : 2-D array, optional

2-D array such that A_eq @ x gives the values of the equality constraints at x .

options : dict

A dictionary of solver options. All methods accept the following generic options:

maxiter

maxiter

disp

disp

For method-specific options, see show_options('linprog') .

Check the provided A_ub and A_eq matrices conform to the specified optional sparsity variables.

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/_linprog_util.py#68
type: <class 'function'>
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