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
val = 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])
A = sp.csr_matrix((val, (row, col)), shape=(2, 3))
The matrix has two rows, one for each constraint, and three columns,
one for each variable in our matrix variable.
The row and col arrays gives the row and column indices
for the 5 non-zero values in the sparse matrix, respectively. The
val array gives the numerical values.
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
rhs = np.array([4.0, -1.0])
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")
