llvm-cbe VS acados

So how does the LLVM C backend work then?

https://github.com/JuliaHubOSS/llvm-cbe

One alternative worth mentioning, though, would be the LLVM C Backend maintained by the Julia community.

If you drop "easily" and "human" (/s) from your requirements list, then the C backend for LLVM might work. Then you can choose any programming language you want that has LLVM 10-compatible frontend.

If you really must have something that compiles in C (e.g. for a platform where you only have a C compiler) there's an LLVM backend that outputs C code: https://github.com/JuliaComputingOSS/llvm-cbe

You can convert llvm bitcode to C and then use C compiler, there is such project https://github.com/JuliaComputingOSS/llvm-cbe .

I'm really surprised that the LLVM C backends have continually been resurrected then abandoned over the years. It's a good solution to this sort of thing and would enable a lot of cool stuff like Rust to weird embedded platforms. The most recent one is the Julia backend: https://github.com/JuliaComputingOSS/llvm-cbe

Check this project out: https://github.com/JuliaComputingOSS/llvm-cbe.

Well IMO it can definitely be rewritten in Julia, and to an easier degree than python since Julia allows hooking into the compiler pipeline at many areas of the stack. It's lispy an built from the ground up for codegen, with libraries like (https://github.com/JuliaSymbolics/Metatheory.jl) that provide high level pattern matching with e-graphs. The question is whether it's worth your time to learn Julia to do so.

You could also do it at the LLVM level: https://github.com/JuliaComputingOSS/llvm-cbe

For interesting takes on that, you can see https://github.com/JuliaLinearAlgebra/Octavian.jl which relies on loopvectorization.jl to do transforms on Julia AST beyond what LLVM does. Because of that, Octavian.jl beats openblas on many linalg benchmarks

You can try your luck with the "resurrected" C backend: https://github.com/JuliaComputingOSS/llvm-cbe

I don't understand why I see so many requests for LLVM-based languages to change around their backend or IR, that seems to be a huge amount of work for comparatively little benefit. The correct thing to do there is to just add support for those to LLVM.

I would check out CasADi (specifically the opti framework) and or ACADOS. To code up a quick MPC in general is not hard, but to squeeze efficiency and exploit sparsity for good real-time performance is a little more involved and these tools really help with that.

Generally, nonlinear MPC uses either IPOPT (an interior point method) or sequential quadtraic programming based approaches (google GURBOI, qpoases, qrqp...). A good python framework is CasADi, or its sister project ACADOS. I think there is also a fair amount of literature on learning MPC cost functions from data you could probably find.

Thanks for the question! My background is in numerical optimization for optimal control. Projects like this https://github.com/acados/acados motivated the development of prometeo. It's mostly about solving optimization problems as fast as possible to make optimal decisions in real-time.

Look at Acados. I didn't use it with Octave, but according the readme it has a interface with Octave.