I'm not sure if this is the problem, but changing the type of a variable in
a function body causes problems for type inference. For that reason, the
first two lines of this code may cause performance issue if you call this
with b and c as matrices. A more Julian idiom for this is to do something
like this:
function simplex(A::Matrix, b::Vector, c::Vector)
(m,n) = size(A);
...
end
simplex(A::Matrix, b::Matrix, c::Matrix) = simplex(A, vec(b), vec(c))
This also avoids copying the data of b and c needlessly.
On Wednesday, November 26, 2014, Emerson Vitor Castelani <
[email protected]> wrote:
> Have you considered the example scsd8.mat?
>
> Em quarta-feira, 26 de novembro de 2014 14h39min14s UTC-2, Pileas escreveu:
>>
>> Result with tic() toc() at the very beginning and at the very end:
>>
>> elapsed time: 0.025719973 seconds
>>
>>
>> Τη Τετάρτη, 26 Νοεμβρίου 2014 11:06:26 π.μ. UTC-5, ο χρήστης Emerson
>> Vitor Castelani έγραψε:
>>>
>>> Well, I am tried to implement a simple version of simplex in Julia and I
>>> have had some troubles. In Julia, my algorithm spends about 30 sec and in
>>> matlab/octave 3 sec for the same problem. So, I saw some tips in order to
>>> get a better performance but the best that I got in Julia was 17-20 sec.
>>> The codes are in attachment. I am new in Julia and the algorithms are
>>> little roughly implemented but they are very similar.
>>>
>>> Thanks
>>>
>>>
>>>
