acados
Metatheory.jl
acados | Metatheory.jl | |
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5 | 5 | |
901 | 366 | |
3.6% | 0.0% | |
9.6 | 5.4 | |
5 days ago | 6 days ago | |
C | Julia | |
GNU General Public License v3.0 or later | MIT License |
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acados
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How to understand Model Predictive Control
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.
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Question about Model Predictive Control (MPC) cost function
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.
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
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.
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Do you know a good free toolbox on mpc control for GNU Octave?
Look at Acados. I didn't use it with Octave, but according the readme it has a interface with Octave.
Metatheory.jl
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[ANN] E-graphs and equality saturation: hegg 0.1
I'd love to see something in the lines of Julia's https://juliasymbolics.github.io/Metatheory.jl/dev/
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Twitter Thread: Symbolic Computing for Compiler Optimizations in Julia
From that example you can see how this makes some rather difficult compiler questions all be subsumed in the e-graph saturation solve. That solve itself isn't easy, it's an NP-hard problem that requires good heuristics and such, and that's what Metatheory.jl, and that's what chunks of the thesis are about. But given a good enough solver, the ability to write such transformation passes becomes rather trivial and you get an optimal solution in the sense of the chosen cost function. So problems like enabling automatic FMA on specific codes is rather simple with this tool: just declare a*b + c = fma(a,b,c), the former is a cost of 2 the latter is a cost of one, and let it rip.
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
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
- From Julia to Rust
- Algebraic Metaprogramming in Julia with Metatheory.jl
What are some alternatives?
pyomo - An object-oriented algebraic modeling language in Python for structured optimization problems.
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
Dagger.jl - A framework for out-of-core and parallel execution
llvm-cbe - resurrected LLVM "C Backend", with improvements
MacroTools.jl - MacroTools provides a library of tools for working with Julia code and expressions.
prometeo - An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
hpipm - High-performance interior-point-method QP and QCQP solvers
Symbolics.jl - Symbolic programming for the next generation of numerical software