Add a set of linear constraints to the model using matrix semantics.
The added constraints are (except that the constraint
sense is determined by the
A argument must be a NumPy dense ndarray or a SciPy sparse
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, which can then be used with
an overloaded comparison operator to build a
TempConstr object. This can
then be passed to
A: The constraint matrix - a NumPy 2-D dense ndarray or a SciPy sparse matrix.
x: Decision variables. 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 second dimension of A.
sense: Constraint senses, provided as a NumPy 1-D ndarray or as a single character. Valid values are , , or . The length of the array must be equal the size of the first dimension of A. A character will be promoted to an ndarray of the appropriate length.
b: Right-hand side vector, stored as a NumPy 1-D ndarray. The length of the array must be equal the size of the first dimension of A.
name: Names for new constraints. The given name will be subscripted by the index of the constraint in the matrix.
A = np.full((5, 10), 1) x = model.addMVar(10) b = np.full(5, 1) model.addMConstr(A, x, '=', b)