Add trait f64 f32 for 1d inverse to ArgminInv

[sdrap]

I tried to implement the f64/f32 standard inverse for the ArgminInv (modification in the implementation of inv for ndarray).
I also added a test for it.

For unknown compilation reasons on my machine (admittedly weird linux conf), I could not run the test nor use the library locally to check if that works. I will have to look later into it.

I am also not sure if this is what was needed (as a fully newcomer into rust).

Short Update: I could run the optimization for scalar function, it seems to work. I am not sure how to get the resulting get_best_param or get_param in the end, it seems that this function changed in 0.8.

[codecov-commenter]

Codecov Report

Patch coverage: 100.00% and project coverage change: -0.03 ⚠️

Comparison is base (0bcc00c) 93.38% compared to head (003c181) 93.35%.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #346      +/-   ##
==========================================
- Coverage   93.38%   93.35%   -0.03%     
==========================================
  Files         119      117       -2     
  Lines       18979    18803     -176     
==========================================
- Hits        17724    17554     -170     
+ Misses       1255     1249       -6     

Impacted Files	Coverage Δ
argmin-math/src/ndarray_m/inv.rs	100.00% <100.00%> (ø)

... and 50 files with indirect coverage changes

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[stefan-k]

I tried to implement the f64/f32 standard inverse for the ArgminInv (modification in the implementation of inv for ndarray). I also added a test for it.

Thanks a lot!! :)

For unknown compilation reasons on my machine (admittedly weird linux conf), I could not run the test nor use the library locally to check if that works. I will have to look later into it.

Have you seen this section of the book? The CI passes, so it should be fine.

Short Update: I could run the optimization for scalar function, it seems to work. I am not sure how to get the resulting get_best_param or get_param in the end, it seems that this function changed in 0.8.

You should be able to get an Option<&f64> via result.state().get_best_param(). Alternatively you should be able to access the best_param field of the state with result.state().best_param. (Yes, this should have better ergonomics ;))

[sdrap]

Thanks, I could get it after a couple of try -> *res.state().best_param.as_ref().unwrap().

BTW: I was wondering how to get a termination done with newton when for instance $f^\prime'/f^{\prime\prime}' is below a certain tolerance level.

I am also thinking about getting a 1d version where the optimum should remain within an interval (for instance when the function is undefined (+infty) out of the bounds. But for that maybe I just implement a special solver since it is arguably quite special.

Many thanks in any cases, the library works like a charm.

[stefan-k]

I was wondering how to get a termination done with newton when for instance $f^\prime'/f^{\prime\prime}' is below a certain tolerance level.

Unfortunately argmin doesn't yet allow users to create their own stopping criteria (#91). You could however add this stopping criterion to the solver and issue a PR, similarly to how it is done here.

I am also thinking about getting a 1d version where the optimum should remain within an interval (for instance when the function is undefined (+infty) out of the bounds. But for that maybe I just implement a special solver since it is arguably quite special.

argmin unfortunately doesn't have a proper concept of bounds yet (apart from Simulated Annealing and Particle Swarm Optimization, where it is quite straight-forward). You can implement this in your own code by implementing the Solver trait. That way you are able to run the solver in the Executor with all its benefits. Feel free to get in touch if you run into problems with this, I'm happy to get some feedback about the ergonomics of implementing a solver.

Many thanks in any cases, the library works like a charm.

Thanks! :)

[stefan-k]

Thanks a lot! :)