[celrod]

I've noticed that R often defaults to much higher tolerances than Julia, even when it's wrappers to the same C library, like cubature.

R cubature[0]: 1e-5 Cubature.jl [1]: 1e-8

The difference for NLopt in R vs Julia is smaller. `NLopt.DEFAULT_OPTIONS`[2] in Julia shows `1e-7` for `ftol_rel`, `xtol_rel`, and `constrtol_abs`, while in R `xtol_rel` is `1e-6` and the others are `0.0`[3]. So, the options at least aren't the same with nlopt. Anyway, I always recommend confirming that you're comparing the same settings.

And of course, in Julia, you'll probably want to `JET.report_opt` your function and fix and glaring performance issues.

NLopt seems like it may be a bit of an exception, but I noticed this is pretty common pattern elsewhere, uniroot[4] being another example, with eps()^(1/4) default tolerance, far higher than Julia root solvers will use.

[0] https://cran.r-project.org/web/packages/cubature/cubature.pd... [1] https://github.com/JuliaMath/Cubature.jl [2] https://github.com/JuliaOpt/NLopt.jl/blob/6ade25740362895bbf... [3] https://cran.r-project.org/web/packages/nloptr/nloptr.pdf [4] https://www.rdocumentation.org/packages/stats/versions/3.6.2...

[BrandonS113]

We take of that by setting all the NLOPT options to be the same across calling languages. We get pretty much the same number of calls in julia, r and c.