Adding constraints to the model
The next step in the example is to add our two linear constraints.
This is done by building a sparse matrix that captures the constraint
matrix:
# Build (sparse) constraint matrix
data = np.array([1.0, 2.0, 3.0, -1.0, -1.0])
row = np.array([0, 0, 0, 1, 1])
col = np.array([0, 1, 2, 0, 1])
The matrix has two rows, one for each constraint, and three columns,
one for each variable in our matrix variable. Note that we multiply
the greater-than constraint by 
to transform it to a less-than
constraint.
We also capture the right-hand side in a NumPy array:
# Build rhs vector
We then use the overloaded @ operator to build a linear matrix
expression, and then use the overloaded less-than-or-equal operator to
add two constraints (one for each row in the matrix expression):
# Add constraints
m.addConstr(A @ x <= rhs, name="c")
