I need to print all my linear constraints to verify the correctness of what I wrote but not knowing the CPLEX library for Python well I would not know how to do it. That’s my part of LP: import cplex …

I need to print all my linear constraints to verify the correctness of what I wrote but not knowing the CPLEX library for Python well I would not know how to do it. That’s my part of LP: import cplex …

I’m migrating an application that formerly ran on IBM’s DoCloud to their new API based off of Watson. Since our application doesn’t have data formatted in CSV nor a separation between the model and data layers it seemed simpler to upload an LP file along with a model file that reads the LP file and solves it. I can upload and it claims to solve correctly but returns empty solve status. I’ve also output various model info (e.g. number of variables) and everything is zeroed out. I’ve confirmed the LP isn’t blank – it has a trivial MILP. Here is

How do you make cplex use a greedy optimization solution as opposed to the optimal solution? Are there parameters you can set or is this not possible? Answer What you can do is to compute the greedy solution yourself and then submit this as a warmstart/MIP start. There are no parameters to force CPLEX to use a greedy algorithm to construct a solution since it is not clear how weights for a greedy algorithm should be chosen for an arbitrary MIP. So for a general MIP, it is not clear what a greedy algorithm should do exactly and whether it