Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.

There are two more things to verify.

To this end, we introduce Poppy, a simple training procedure for populations.

We have not trained our models with sensitive or private data, and we emphasize that our model's direct L( n) other than the constant one as long as g (n) and l ( n) are positively correlated. The results for the baselines AdaSubS, kSubS, BC, CQL, DT, and HIPS with learned models were copied from [18]. The total number of GPU hours used on this work was approximately 7,500. We used 6 CPU workers (AMD Trento) per GPU. In the latter case, completeness cannot be guaranteed.

We introduce Policy Optimization with Multiple Optima (POMO), anend-to-end approach forbuildingsuchaheuristic solver.POMO isapplicable to a wide range of CO problems.

The measure captures errors due to absent predicted requests as well as unpredicted actual requests; hence, predicted and actual inputs can be of arbitrary size.