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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
The source code of lyapunov was one of the very first functions of DynamicalSystems.jl. It does not use the set_state! function for parallel integrators. That is a mistake (and in fact made us miss a bug).
One should re-write the rescale! function of the code to simply generate the new reference state and the just call set_state!.
I was really young programming-wise when I wrote parallel_integrator of in-place continuous systems. I wrote it as having a matrix state, where each column is each state of the dynamical system. I'm not sure why I went with this route instead of the route of having it a vector of states. I'm certain that I have benchmarked both cases extensively and chose the one with most performance.
We have established the validity of our unified embedding methods because we even wrote a paper utilizing them in detail and comparing them with each other. There isn't any doubt that they work. However, the way we have written our tests is brittle, because (due to chaos) running the test on different machines may make them fail. For example while working on PR #93 I've seen things like:
The source code of
lyapunovwas one of the very first functions of DynamicalSystems.jl. It does not use theset_state!function for parallel integrators. That is a mistake (and in fact made us miss a bug).One should re-write the
rescale!function of the code to simply generate the new reference state and the just callset_state!.