Hellooo Emil !!!
Well, I just tried something and it ended upi crashing Sage, so I can just
advise you to create all your variables in the first LP from the start,
*then* to copy the MixedIntegerLinearProgram object. Of course it is a bad
answer :-)
John Perry was the one who needed this
Hi Nathann,
Thanks for writing the MILP class - it works very well. Now, I can do:
x = lp.new_variable()
Is there any way to do something like
x = lp.get_existing_variables()
?
I'm working on some graph theoretic stuff: I'm solving two LPs for
each graph, for as many graphs as I can. - Emil.
On Monday, May 14, 2012 7:32:25 PM UTC-5, Emil wrote:
lp = MixedIntegerLinearProgram(maximization=True)
x = lp.new_variable()
Then I do:
nlp = copy(lp)
x = nlp.new_variable()
The variable 'x' now seems to contain different variables. So I cannot
add any constraints that use the
Next issue is that the Gurobi backend doesn't support the copy:
AttributeError: 'sage.numerical.backends.gurobi_backend.GurobiBacke'
object has no attribute 'copy'
Any idea how much work this would be to do?
(I can now do what I wanted to do before, at least with GLPK.)
Emil
--
To post to
Hellooo !!
Next issue is that the Gurobi backend doesn't support the copy:
Oops ^^;
Any idea how much work this would be to do?
Oh, it's usually quite straightforward to implement such things.
Usually the feature already exists in the solver's C api, and all the
work that needs to be
OK I'll take a look :)
On 15 May 2012 21:55, Nathann Cohen nathann.co...@gmail.com wrote:
Hell Emil !!
Any chance you could make a patch? :) (I'd volunteer myself, but I
would probably mess it up!)
H I could, but this patch is so local that it really is an
ideal occasion
