m.addConstrs((((quicksum(x[i, j] * A[j, :] for i, j in feasible_arcs)) >= rhs)), name='slow_constraint' )

m.addConstrs(((((np.dot(x[i, j] , A[j, :]) for i, j in feasible_arcs)) >= rhs)), name='slow_constraint' )

I should have added a parenthesis closing np.dot in the second constraint but it does not generate a valid constraint as we expected

Hi Shiyao,

If I understand correctly, you want to add the constraint $$XAe \leq d$$, where $$X \in \mathbb{R}^{m \times n}$$ is a matrix of variables, $$A \in \mathbb{R}^{n \times p}$$ is a coefficient matrix, $$e \in \mathbb{R}^p$$ is the vector of ones in dimension $$p$$, and $$d \in \mathbb{R}^p$$ is your RHS vector. If so, the row-wise sum of $$XA$$ (i.e., $$XAe$$) is an $$m$$-dimensional vector, which doesn't match the RHS dimension of $$p$$. Could you clarify which constraints you're trying to add? Maybe this should be the column-wise sum of $$X A$$?

Also, is the following condition correct when creating the feasible_arcs list?

i + j % 5 != 3

I wonder if there should be parentheses around i + j. As written, the feasible_arcs list is not very sparse, containing 19840 out of a possible 20000 arcs.

Thanks,

Eli

Hi Eli,

Thank you so much for you reply!

I do appologize for typos in code and type settings. We are trying to find out how to type latex here.

For your first question regarding to the dimension mismatch, you are absolutely correct, I mean column sum. Following your notation, I tried to add a constraint e^T XA <= d, where e is a n * 1 column vector of ones.The dimension of X, A and d are exactly as you stated above.

For the feasible_arcs list, you are right again. It should be parenthesised like

(i + j) % 5 != 3

For decision variables, I could have used addMVar method to creat a two dimensional decision variable. But in our actual problem, len(feasible_arcs) is less than 1% of m*n. So we want to create decision variables from list to reduce problem size.

The reason why I did not create very sparse decision variables is that I want to show that adding constraint takes a significant amount of time. Alternatively, we could increase the size of X and A and in the list,

(i + j) % 100 == 1

Shiyao

Hi Shiyao,

Thanks for the clarification. I think you could add these constraints more efficiently if you had a mapping that defines the inbound arcs to a given node. E.g.:

inbound = {}for i,j in feasible_arcs:    if j in inbound:        inbound[j].append(i)    else:        inbound[j] = [i]

Then, you can add the constraints as follows:

m.addConstrs((quicksum(A[j,k] * quicksum(x[i,j] for i in inbound[j]) for j in inbound) <= rhs[k]) for k in range(num_constraints))

Note that because the matrix $$A$$ is completely dense in your code, the number of nonzeros in the coefficient matrix is equal to the number feasible arcs times the number of constraints. Thus, with the full 20000 arcs, the coefficient matrix contains 20 million nonzeros. Hopefully the model building is faster for more sparse arc sets.

Eli