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 existing variables. Or is there some
> way to do this? Thanks,
>
x *should* contain different variables, for two reasons. First, nlp already
has a variable (a copy of the one you created for lp), so if you ask nlp to
create a new variable for it, it won't return the variable lp created
earlier.
Second, after copying lp to nlp, you might want to change some variables in
one from real to integer, or vice-versa.
Also, I don't think Sage has ever let you create variables & add
constraints that way. I don't know why, but if I want a variable with a
compact notation, I've found MILP lets you do it this way:
sage: x, y = lp[0], lp[1]
but NOT
sage: x, y = lp.new_variable(), lp.new_variable()
You'll get variables alright, but you can't add constraints using the
second. The first works fine.
To add constraints, I usually do the following:
sage: lp = MixedIntegerLinearProgram(maximization=False)
sage: lp.add_constraint(2*lp[0] + 3*lp[1] <= 1)
sage: nlp = copy(lp)
sage: nlp.add_constraint(3*lp[0] - 2*lp[1] <= 1)
Or, if you like, use x, y, etc., defining them as I did above (the FIRST
way).
regards
john perry
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