csips VS or-tools

I actually just finished implementing an extremely simple Integer Linear Program solver in Python as an educational exercise, wrapping scipy's linprog function to solve the linear relaxation. It has an expression syntax so you don't have to specify the matrix and vectors for the standard form, and it does branch-and-cut on the linear relaxation

https://github.com/cwpearson/csips

Small NP-hard problems aren't actually that bad. You can usually formulate them as eg a integer programming problem or a SMT problem, and throw an off-the-shelf solver at them.

You only need to learn the solver once, and you can re-use it for all kinds of problems. (Assuming that your instances don't have to be solved with low latency. Eg only as part of your level generation process, or at most when loading a randomly generated level, but not every frame or so.)

https://developers.google.com/optimization has a decent collection of tools.

Just saw that it looks like an upcoming release of OR-Tools might include reified tables: https://github.com/google/or-tools/commit/94f3d9b46870e7ea04...

Google OR - Tried to represent a solution to be a 5 dimensional matrix with an hour granularity. Dimensions are stations, program, project manager, day and time. If matrix[station][program][project manager][day][time] = 1, then that set is assigned, otherwise not. The main issue encountered here is about time slots, as they are not necessarily on a per hour basis. We tried time slots to be in a 5-minute interval. However, constructing the constraints that would adhere to each programs duration was proven to be difficult.

If you are just interested in getting a solution or for having a reference solver: There is a sudoku example in the OR-Tools package that uses constraint programming.

Not sql, but check out google’s OR-Tools. Hardly ever gets mentioned but looks very capable for some applications. https://developers.google.com/optimization

Then there's also the Google's solution: https://developers.google.com/optimization/

start enumerating the properties you think the solution to your problem should have. once you have this, you should be able to reformulate those properties as constraints and then you can just plug this into a combinatorial solver such as https://developers.google.com/optimization