BrandonS113

8 ms·

31.
by BrandonS113 4y ago
I have been thinking the same and had similar timing experiences. As Julia is lower level than R/Python, there is a lot of annoying things to take care of that are not needed in R/Python. And then why not use, say Rust? Or just Rc
32.
by BrandonS113 4y ago
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.
33.
by BrandonS113 4y ago
Of course it is, sometimes one needs to pass data represented as R objects (like zoo) to R functions, and receiving something that is an R object and to be worked on by R calls before being passed on. That is very clumsy to manage with RCal
34.
by BrandonS113 4y ago
sure, for parameters par (what is optimized for), data vector x (typical length from 10 to 20), constants n and n2, a typical function is if((1 - par[3]^2)<0 ) return(100) if(par[1] + par[2] \* par[5] \* sqrt(1 - par[3]^2)<0)
35.
by BrandonS113 4y ago
Yes, well no gradients. It a simple parametric function (6 parameters) applied to a vector of lengths 10 to 20. All vectorizes in the language of R. All powers, logs, exponentials, ratios, and sums. Large number of local maxima, and places
36.
by BrandonS113 4y ago
no. the code spends 99% of its time in 5 lines in the objective function. And my usual experience is that Julia is much faster than R. Just not always, apparently.
37.
by BrandonS113 4y ago
No, nlopt. Why we could easy port from R to Julia as nlopt exists for both (its c)
38.
by BrandonS113 4y ago
1.5 is on the low side. I use Python, R, Julia, and Latex professionally. Python for op system/internet/data, R for stats and Julia for numerical calculations (some very large scale). So know the useful parts of all 4.
39.
by BrandonS113 4y ago
That is exactly the issue. No language comes close to the richness of the R statistical package ecosystem.
40.
by BrandonS113 4y ago
If doing data science, I find Julia's tools to be inferior to Python and R. But in my work, when it comes to long computations, not only does Julia usually vastly outperform both, we write Julia code faster with fewer errors.
41.
by BrandonS113 4y ago
That is harsh. I know PhDs doing comp science work, hey with PhDs in comp science, coming to same conclusion. Python is an excellent language. But coding in numpy is not its strength.
42.
by BrandonS113 4y ago
Just now I was thinking of moving a long calculation from R to Julia (non-linear optimisation of a simple function with multiple local minima, for a lot of different datasets). No loops. Embarrassingly parallel. And to my great surprise, R
43.
by BrandonS113 4y ago
I have a lot of respect for someone doing that. I really tried to make my CV work in latex and failed. But for anything I do latex in better than the alternatives I have tried, word, markdown, html+css, powerpoint. All of these might be bet
44.
by BrandonS113 4y ago
I have a mac that has only been upgraded between what 20 major versions and migrated between 6 macs since 2003, never an issue small or large. Never reinstalled from scratch. You must have had bad luck.
45.
by BrandonS113 4y ago
I have never found a silent error. If bibtex is not happy, it complains loudly. I use the style files given by the publishers and it always comes out OK. Never had a publisher complain about the bibliography when using their styles, and the
46.
by BrandonS113 4y ago
I agree with much of what you say. I certainly find bibtex frustrating. On your specific points 1. I might be the only one, but I do sometimes, for the reasons you say. To debug it. But yes, debugging bibtex files is a a real pain. 2. yes,
47.
by BrandonS113 4y ago
This is the first time I have seen such a complaint against bibtex. Yes bib files are finicky to write correctly, and bibtex can be a pain to use. But bibliographies are never "imprecise, missing important info or plain wrong." I&
48.
by BrandonS113 4y ago
I map the function keys to virtual desktops, but same idea. And CAPLOCK to Alfred to select apps. Super easy on Mac, and difficult to do on Linux, the main reason I don't like Linux desktops. To each their own.
49.
by BrandonS113 4y ago
I'm now visiting a brand new office building in London and it is stunning. Not minimalist, brutalist, ornate. And unique. And everybody who works in it seems to love it. I want to work there. What bugs me is how most new buildings are
50.
by BrandonS113 4y ago
I go through cycles with the Economist, months when I can't stand it, and months when I read most pieces. Think it is me and the Economist. That said, I have not found any consistently better alternative in English or any of the other
51.
by BrandonS113 4y ago
R has much much better statistical packages that R, if it is statistics, you can probably find a package in R to do it, not same with python. And the programming language is much better for statistics than numpy/pandas if a package is
52.
by BrandonS113 4y ago
I got the same Streacom DB4, with a fast processor, and Ubuntu. It is wonderful to have a system running at full speed for days on all cores, and no sound. None. No fans. And it looks good, first PC I ever kept on my desk.
53.
by BrandonS113 4y ago
I use Jekyll for a few sites and it works perfectly. Design is bootstrap and so nothing to do with Jekyll and it always compiles the site, takes a few seconds. Works fine on my mac and linux. Would I have picked Jekyll today? no. Is it wort
54.
by BrandonS113 4y ago
The source code is linked at the very top, under the heading "Computation Speed": https://modelsandrisk.org/appendix/speed_2022
55.
by BrandonS113 4y ago
My take from the article and the discussion below is that libraries is what makes R the best. Is that true? Will python and Julia then catch up? Or is is just best to use R, warts and all?
56.
by BrandonS113 4y ago
that is hard, but its quite easy to call R inside Julia.
57.
by BrandonS113 4y ago
If you work in economics, how could you miss Nobel price winner Thomas Sargent's QuantEcon? I know of lots of econ graduate students who use Julia.
58.
by BrandonS113 4y ago
well Matlab cannot read compressed CSV files, a problem when uncompressed files are very large. And it is very slow as the article showed. But visualisation. Please, R with ggplot is miles ahead of Matlab.
59.
by BrandonS113 4y ago
They did mention that in article. " Not surprisingly, it has been adopted in high quality projects, such as Quantitative Economics with Julia, popularised by Thomas Sargent (Perla et al., 2022)."
60.
by BrandonS113 4y ago
An interesting writeup. Old versus new. So Julia is the language for those doing new things, Python and R for those with specialized applications and Matlab those stuck in legacy teams.

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