alt Hacker NewsIt kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.
It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.
I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math.
https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli...
If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm very carefully converting the equations I've scribbled down on paper into code and always a little nervous that I've accidentally split one variable into two names or used the wrong equality operator and am going to end up hating life, or accidentally assigned x = sp.Symbol("y") somewhere.
The Julia version is just plain beautiful. There's no ceremony other than the three @parameters, @variables, @mtkcompile macros. It lives in its own little world where you don't have to constantly watch your back to make sure you haven't duplicated a symbol somewhere.