On Tue, Jul 7, 2020 at 12:17 PM chandra chowdhury
<pc.chandr...@gmail.com> wrote:
>
> For max=1, there is no solution but for max=2 it has solution.
> How can we find upper bound of max using loop for similar
> problem?
>
> p = MixedIntegerLinearProgram(solver='GLPK')
> x = p.new_variable()
> p.set_binary(x)
> p.set_objective(x[0] )
> p.add_constraint(x[0]+x[1],max=1)
> p.add_constraint(x[0],min=1)
> p.add_constraint(x[1],min=1)
>
> print p.show()
> print p.solve()
> A=p.get_values(x)
> print A
in this case you don't even need a loop, you can compute the minimal
"max" directly:
sage: p = MixedIntegerLinearProgram(solver='GLPK')
....: x = p.new_variable()
....: p.set_binary(x)
....: p.set_objective(-x[0]-x[1])
....: p.add_constraint(x[0],min=1)
....: p.add_constraint(x[1],min=1)
....: p.show()
....: p.solve()
....: p.get_values([x])
....:
Maximization:
- x_0 - x_1
Constraints:
1.0 <= x_0
1.0 <= x_1
Variables:
x_0 is a boolean variable (min=0.0, max=1.0)
x_1 is a boolean variable (min=0.0, max=1.0)
-2.0
[{0: 1.0, 1: 1.0}]
>
> On Mon, Jul 6, 2020 at 11:06 PM chandra chowdhury <pc.chandr...@gmail.com>
> wrote:
>>
>> Hi,
>> I am trying to solve MILP using Sage. Problem is if there is no solution
>> it gives an error. Instead of an error, I want to change constraints
>> (like if initial max=100 which does not give solution, new max=101 which may
>> give solution)
>> and run the solver automatically.
>>
>> p = MixedIntegerLinearProgram(solver='GLPK')
>> ....
>> p.solve()
>>
>> sage.numerical.mip.MIPSolverException: GLPK: Problem has no feasible
>> solution
>
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