eqp_kktfact(H, c, A, b)

Solve min 1/2 x.T H x + x.t c subject to A x + b = 0 using direct factorization of the KKT system.

Parameters

H : sparse matrix, shape (n, n)

Hessian matrix of the EQP problem.

c : array_like, shape (n,)

Gradient of the quadratic objective function.

A : sparse matrix

Jacobian matrix of the EQP problem.

b : array_like, shape (m,)

Right-hand side of the constraint equation.

Returns

x : array_like, shape (n,)

Solution of the KKT problem.

lagrange_multipliers : ndarray, shape (m,)

Lagrange multipliers of the KKT problem.

Solve equality-constrained quadratic programming (EQP) problem.

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/_trustregion_constr/qp_subproblem.py#20
type: <class 'function'>
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