DenseSharedProjQCQP#

class DenseSharedProjQCQP(A0, s0, c0, A1, s1, Plist, Pstruct=None, A2=None, B_j=None, s_2j=None, c_2j=None, verbose=0)[source]#

Bases: _SharedProjQCQP

Dense QCQP with projector-structured constraints.

Dense analogue of SparseSharedProjQCQP; uses scipy.linalg for Cholesky factorization. Inherits full problem specification from _SharedProjQCQP.

Parameters:

  • A0 (ArrayLike) – Objective quadratic term.

  • s0 (ArrayLike) – Objective linear term.

  • c0 (float) – Objective constant.

  • A1 (ArrayLike) – Left quadratic factor in projector constraints.

  • s1 (ArrayLike) – Projector constraint linear term.

  • Plist (list[ArrayLike]) – list of 2D arrays representing the projector matrices P_j.

  • A2 (ArrayLike | None, default None) – Right quadratic factor (defaults to identity if None).

  • verbose (int, default 0) – Verbosity level.

  • Pstruct (_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None)

  • B_j (list[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]] | None)

  • s_2j (list[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]] | None)

  • c_2j (_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None)

Notes

  • All quadratic matrices must be dense (or convertible) for this class.

  • General constraints can be supplied via the base constructor if extended.

Methods Summary

is_dual_feasible(lags)

Check PSD feasibility of A(lags) via dense Cholesky attempt.

Methods Documentation

is_dual_feasible(lags)[source]#

Check PSD feasibility of A(lags) via dense Cholesky attempt.

Parameters:

lags (FloatNDArray) – Full Lagrange multiplier vector.

Returns:

True if Cholesky succeeds, False otherwise.

Return type:

bool