I myself do not care much about Julia or python speed since I can write
fast code in Cython (C/C++ speed with Python syntax sugar). There are
several features in Julia I like much (and I think they can be good selling
points).
* CFFI: great that we can call C function easily.
* Parallel: I very like Julia parallel syntax. Never tried to write Julia
parallel code but once I read the code, just have feeling that the parallel
usage is very natural
* macro: @time bla_bla --> I like the macro in the same line
* Julia syntax for computing is much more concise than python.
Things I don't like in Julia
* developers and user encourage to use 'using' to import all methods. I
prefer to see exporting importing in Python (from numpy import dot, ...)
* Julia's string stuff seems very strict to me. For example I wish Julia
has: a = "my 1st string; b = 'my 2nd string', c = a + b
* declare type by `x::Int`. I myself prefer to use 'x : int' typehint in
Python3.5.
* warming up time in Julia is still slow for me.
Hai
On Wed, Sep 30, 2015 at 1:01 PM, David Anthoff wrote:
> When you have matrices with special structures and symmetries, Julia can
> encode that information in the type of the matrix, and in those cases the
> backslash operator should actually be more efficient than in Matlab because
> there is no need to “guess” what the best algorithm might be. My
> understanding is that no such thing exists in Matlab. Having said that,
> take this with a grain of salt, this is not my area of expertise (neither
> in Julia nor in Matlab).
>
>
>
> The relevant Julia manual section is
> http://docs.julialang.org/en/latest/manual/linear-algebra/.
>
>
>
> *From:* julia-users@googlegroups.com [mailto:julia-users@googlegroups.com]
> *On Behalf Of *Art Kuo
> *Sent:* Wednesday, September 30, 2015 9:30 AM
> *To:* julia-users
> *Subject:* Re: [julia-users] Re: What's the reason of the Success of
> Python?
>
>
>
> > (x=inv(A)*b) than it is in many other languages. Julia has at least
> this
>
>
> You shouldn't ever do this (in either Julia or Matlab, or any language),
> it is ill-conditioned for general matrices. I think the Matlab function
> is linsolve.
>
> Perhaps more precise to say "A\b is always faster than inv(A)*b, and more
> accurate if A is ill-conditioned." I also usually prefer Matlab's backslash
> or mldivide, A\b, over the more specific linsolve, because backslash
> chooses the right solver (including linsolve) most of the time, for systems
> that are sparse, over- or under-determined, etc. Julia also implements
> similar functionality, but I doubt if it is as optimized as Matlab, which
> has many years' advantage. As for the accuracy advantage, it is true that
> inv(A)*b can go wrong, and A\b will go less wrong. But Ill-conditioned A is
> usually an indicator that neither approach will be great.
>
>
>
