or-tools VS rayon

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

Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.

rayon: Async CPU runtime for parallelism.

In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.

With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer

(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)

I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :

Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.

Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.