but it probably will potentially be negative
also I think there are many local minima when computing t (since planets tend to go in circles)... so maybe not such a good idea, i apologize
line 6 of my gist double–counts the current position of the target
when I fix that I get a perfect solution about half the time, and an invalid one the rest
works pretty well, actually: http://db48x.net/temp/plotting%20a%20course%20sometimes%20works%20correctly-2021-07-30_16.11.58.webm
@stefan-k, thank you for your advice!
db48x @db48x yawns
it is hours past my bed–time, but I can’t help but play with this: http://db48x.net/temp/plotting%20a%20course%20sometimes%20works%20correctly-2021-07-30_16.59.32.webm
I'm afraid there is no way to get more information about Lapack out of the solver unless you edit the library code... At first glance I'd expect that one of the
ArgminOp methods is returning something containing NaNs or INFs. Have you tried printing the return values of those methods during a solver run?
Hello @stefan-k I went through argmin-rs/argmin#2 and I think argmin-rs/argmin#184 will fix it. The steepestdescent.rs and hagerzhang.rs examples both seem to work as expected now. I think there was an error with the Wolfe condition which resulted in early termination before the iteration started, since the first condition can happen to be satisfied if one chooses good starting parameters. Cheers
That would be great! :) Currently there is only one population based method (PSO) in argmin, therefore the architecture of argmin may not be ideal yet. Let me know if you run into any problems, we'll find a solution. May I ask which paper you're reading? If I find some time I might read it too.
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Regarding your other PR: Would it be fine for you if I merged it into master instead of doing a patch release for 0.5? I tried to rebase it but honestly I have no idea what happened :D
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Hey @stefan-k. We developed a crate that provides generalized (hyper-) dual numbers which allow you to compute (partial) derivatives without the need to implement analytical derivatives. Might be interesting for
argmin-rs. The crate can be found here
Hey @g-bauer , thanks for letting me know! This looks great, I will consider integrating this into argmin (after #198) :)
Hey @stefan-k, I was using FiniteDiff today and have been experiencing some strange results. It seems the values are correct but
central_diff is not returning me the full f64, it is only returning me the values with two decimal places. Is this expected behavior or is there some configuration I need to set? Im pretty new to rust so I may be missing something.
The file is 250Mb so Gitter isnt letting me upload it, from https://earth-info.nga.mil click EGM2008 Spherical Harmonics, inside there is a file called EGM2008_to2190_TideFree, add .txt extension to it and the code should run! Let me know if I am just missing something silly (which I probably am haha!)
I think the numbers are correct. If I take the numbers printed by your closure and do the math manually (in google), I get the exact same result:
(62475811.3671359-62475811.36713561)/(2* sqrt(2.2204460492503131e-16)) = 9.75. This is either correct or there are numerical issues (in that case I'm afraid there's not much I can do). There could still be a problem in finitediff, but so far I couldn't find any indication. If you still think there is something wrong with finitediff then it would be great if you could provide a very minimalistic example because it is difficult for me to understand code from a field I'm not used to :)
Can somebody share a working code example of using NelderMead algorithm? Whatever I do, the program just crashes
or even don't compile
@nikmas-dev Which version of argmin are you using and which example is your code based on? The examples on the main branch on github are based on a yet unreleased version of argmin and hence won't work with 0.5.1 (and below). You should either use the main branch and base it on the current example in github or use version 0.5.1 and this example. Make sure that the number of initial parameter vectors that you provide is
n+1 where n is the number of dimensions of your problem.
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@stefan-k, do you plan to implement the Powell method like in scipy minimize: https://docs.scipy.org/doc/scipy/reference/optimize.minimize-powell.html#optimize-minimize-powell ?
@stefan-k, do you plan to implement the Powell method like in scipy minimize: https://docs.scipy.org/doc/scipy/reference/optimize.minimize-powell.html#optimize-minimize-powell ?
It seems very commonly used and simple
Glad you got it to work! Powell's method is certainly in the scope of argmin, but there are currently no plans to implement it in the near future. Would you mind opening an issue on Github about it? That way I won't forget about your request :) You're of course also invited to issue a pull request :)
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