On Wednesday, May 18, 2016 at 12:58:23 PM UTC+1, Jeroen Demeyer wrote:
>
> On 2016-05-18 13:44, Dima Pasechnik wrote:
> > Do you mean that you want a cleaner
> > interface for p.polyhedron() in the
> > case when p has more than 1 variable?
> Yes. Example:
>
> sage: P =
On 2016-05-18 13:44, Dima Pasechnik wrote:
Do you mean that you want a cleaner
interface for p.polyhedron() in the
case when p has more than 1 variable?
Yes. Example:
sage: P = MixedIntegerLinearProgram(solver="PPL")
sage: x = P.new_variable(nonnegative=True)
sage: y =
On Wednesday, May 18, 2016 at 10:35:53 AM UTC+1, Jeroen Demeyer wrote:
>
> On 2016-04-19 15:00, Dima Pasechnik wrote:
> > here is an example:
> >
> > sage: P=3*polytopes.cube()
> > sage: (p,x)=P.to_linear_program(solver='ppl',return_variable=True)
> > sage: p.set_objective(x[0]) #
On 2016-04-19 15:00, Dima Pasechnik wrote:
here is an example:
sage: P=3*polytopes.cube()
sage: (p,x)=P.to_linear_program(solver='ppl',return_variable=True)
sage: p.set_objective(x[0]) # maximizing x[0] over 3 times inflated unit
cube
sage: p.solve()
3
sage: p.add_constraint(x[0]==3) # restrict
here is an example:
sage: P=3*polytopes.cube()
sage: (p,x)=P.to_linear_program(solver='ppl',return_variable=True)
sage: p.set_objective(x[0]) # maximizing x[0] over 3 times inflated unit
cube
sage: p.solve()
3
sage: p.add_constraint(x[0]==3) # restrict to the optimal face
sage: Q=p.polyhedron()
On Tuesday, April 19, 2016 at 11:26:23 AM UTC+1, Jeroen Demeyer wrote:
>
> Hello,
>
> I have a MixedIntegerLinearProgram where all variables are integers. I
> want to minimize one of the variables, but I need *all* solutions, i.e.
> all assignments to the variables which minimize the goal
