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Model.addMQConstr()

Add a quadratic constraint to the model using matrix semantics. The added constraint is (except that the constraint sense is determined by the sense argument). The Q argument must be a NumPy ndarray or a SciPy sparse matrix.

Note that you will typically use overloaded operators to build and add constraints using matrix semantics. The overloaded @ operator can be used to build a linear matrix expression or quadratic matrix expression. An overloaded comparison operator can then be used to build a TempConstr object, which is then passed to addConstr.

Arguments:

Q: The quadratic constraint matrix - a NumPy 2-D ndarray or a SciPy sparse matrix.

c: The linear constraint vector - a NumPy 1-D ndarray. This can be None if there are no linear terms.

sense: Constraint sense. Valid values are , , or .

rhs: Right-hand-side value.

xQ_L: Decision variables for quadratic terms; left multiplier for Q. Argument can be an MVar object, a list of Var objects, or None (None uses all variables in the model). The length of the argument must match the size of the first dimension of Q.

xQ_R: Decision variables for quadratic terms; right multiplier for Q. The length of the argument must match the size of the second dimension of Q.

xc: Decision variables for linear terms. Argument can be an MVar object, a list of Var objects, or None (None uses all variables in the model). The length of the argument must match the length of c.

name: Name for new constraint.

Return value:

The QConstr object.

Example usage:

  Q = np.full((2, 3), 1)
  xL = model.addMVar(2)
  xR = model.addMVar(3)
  model.addMQConstr(Q, None, '<', 1.0, xL, xR)

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