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Write julia version of basic transition dynamics #23
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I made a few tweaks to get it running, and changed the time range. to something small (which executes fine now). It is possible that with a large T it really was unstable (at least with that time-stepping method).
Looking like some great progress. I am still not 100% sure about the composition of the operators.
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The latest commit isn't compiling for me. the Also, can I move some of this to Julia files? Do you have atom up and running now? |
Sure, I haven’t upload the leatest one. The one you saw is uploaded before
we fixed The issue for DifferentialEquations.jl. I have fixed it, and I
start using Atom now.
On Wed, May 16, 2018 at 4:25 PM Jesse Perla ***@***.***> wrote:
The latest commit isn't compiling for me. the L_2 referes to the L_tilde,
wich hasn't been implemented yet. Try to recompile it and you will see.
Also, can I move some of this to Julia files? Do you have atom up and
running now?
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Sounds good. I pushed a variation of the code for atom that you should get as an update. You can delete it, or maybe check out the only change of substance (where I pass in the algorithm type as a setting |
Steven, In particular, make sure to pull from the server to get the copy I put up there,
Close the issue and push to github when you think it is complete, and we can discuss next steps |
I changed our function to go backwards in time and tested different solving algorithm for stability. I wrote a few comments to show the stability of each algorithm. Most of them are quite stable with our numerical setup, but I could not figure out how to properly plot graphs. It seems like the plotting requests to adjust the corresponding time axis. |
Great! The code looks very clean. I think the plotting problems are a bug: SciML/DifferentialEquations.jl#300 but could be wrong. I will close the bug and we can do the plotting/etc. separately. |
We should Julia version of one of our basic dynamics tests, such as https://github.com/econtoolkit/continuous_time_methods/blob/master/matlab/tests/discretize_time_varying_univariate_diffusion_test.m#L47
I wrote this up as https://github.com/JuliaDiffEq/PDERoadmap/blob/master/BellmanDiffusion.md which you can modify as you see fit.
L Q
product in these simple cases, we can test this without the operator composition but still using the Julia DIffEq setup.L Q
operator as a https://docs.julialang.org/en/stable/stdlib/linalg/#Base.LinAlg.TridiagonalL Q
at the terminalT
, solve the system withu = L \ b
. This will exploit the tridiagonal solver.Given that matrix as a function of the time, we can do something along the run of
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