Actually, in my case one can just rely on the Smith normal form (I
wonder why PPL/GLPK do not try that first). More precisely, one can
consider the lattice in Z^3 given by the triples of values of my
equations

(x[0] - x[1] + x[2] - x[3] - x[4] - 2 * x[6],
 x[0] - x[1] + x[2] - 2 * x[4] - x[5] - x[6],
 2 * x[0] - x[3] - x[4] - x[5] - x[6])

where x is in Z^7. One obtains that the lattice is equal to the
vectors in Z^3 with even sum of coordinates (ie generated by (0,1,1),
(1,0,1), (1,1,0)). The lattice does not contain the particular
solution (2, 2, -1) I am looking for.

On Wed, 21 May 2025 at 20:23, Vincent Delecroix
<20100.delecr...@gmail.com> wrote:
>
> Sure: take x0=1/2.
>
> On Wed, 21 May 2025 at 20:20, Dima Pasechnik <dimp...@gmail.com> wrote:
> >
> > I forgot - does Sage do the Minkowski decomposition into compact and 
> > non-compact parts (so you do have a ray in your polyhedron)
> > Is it even possible to define over Q a non-compact polyhedron without 
> > integer points?
> > (sorry, my geometry of numbers knowledge is pretty bad)
> >
> >
> >
> > On Wed, May 21, 2025, 12:52 Vincent Delecroix <20100.delecr...@gmail.com> 
> > wrote:
> >>
> >> Note that for triangulating, sage does not help
> >>
> >> sage: M.polyhedron().triangulate()
> >> Traceback (most recent call last):
> >> ...
> >> NotImplementedError: triangulation of non-compact polyhedra that are
> >> not cones is not supported
> >>
> >> On Wed, 21 May 2025 at 18:39, Dima Pasechnik <dimp...@gmail.com> wrote:
> >> >
> >> > PS. You also have not set an objective function, not sure, but it could 
> >> > be why you have no termination
> >> >
> >> > On Wed, May 21, 2025, 11:37 Dima Pasechnik <dimp...@gmail.com> wrote:
> >> >>
> >> >> It should be possible to construct the polyhedron determined by the 
> >> >> feasible set of the LP, triangulate it, and do simplex by simplex or 
> >> >> perhaps use various results relating volumes and presence of integral 
> >> >> points in polyhedra.
> >> >>
> >> >> On Wed, May 21, 2025, 11:27 Vincent Delecroix 
> >> >> <20100.delecr...@gmail.com> wrote:
> >> >>>
> >> >>> Dear all,
> >> >>>
> >> >>> I have a 7 variables 3 constraints linear program that I want to solve
> >> >>> with integers
> >> >>>
> >> >>> x = M.new_variable(integer=True, nonnegative=True)
> >> >>> M.add_constraint(x[0] - x[1] + x[2] - x[3] - x[4] - 2 * x[6] == 2)
> >> >>> M.add_constraint(x[0] - x[1] + x[2] - 2 * x[4] - x[5] - x[6] == 2)
> >> >>> M.add_constraint(2 * x[0] - x[3] - x[4] - x[5] - x[6] == -1)
> >> >>>
> >> >>> However, with both
> >> >>>
> >> >>> M = MixedIntegerLinearProgram(solver="PPL")
> >> >>>
> >> >>> and
> >> >>>
> >> >>> M = MixedIntegerLinearProgram(solver="GLPK")
> >> >>>
> >> >>> The command M.solve() does not terminate in reasonable time... I do
> >> >>> not expect the system to have solutions, but I would like a proof of
> >> >>> it.
> >> >>>
> >> >>> One subtlety of the system is that there are (infinitely many)
> >> >>> positive integral solutions of the homogeneous version (ie linear
> >> >>> combination == 0). I wondered if that was the reason why it is harder
> >> >>> for a solver.
> >> >>>
> >> >>> If anyone knows of an alternative way to provide an open source
> >> >>> computer assisted proof that there is no solution I would be
> >> >>> interested.
> >> >>>
> >> >>> Best
> >> >>> Vincent
> >> >>>
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