[SCIP] Problem with integer variables getting not integer solution

[Jakob Witzig]

Hi Luciana,

which SCIP version do you use? I tried your model with the latest bugfix 
release (3.2.1) and everything works fine, I got the following solution:

objective value:                     6431.66666666667
x1                                                  3 (obj:1280.55555555556)
x2                                                  2   (obj:1295)


Cheers,
Jakob

On 29.07.2016 00:53, Luciana Garcia Richter wrote:
> Hi,
>
> I have defined a problem with integer variables (it is an instance of
> a knapsack problem).
> I have included the print for the original problem, the transformed
> problem and the solution.
> As you can see, all variables are defined as integer, but the solution
> I get is far away from an integer value (e.g. 2.6 for x1 or 0.4 for
> x3)
> Is there something I'm missing?
>
>
> #########################  SCIPprintOrigProblem  ############################
> STATISTICS
>    Problem name     : Knapsack
>    Variables        : 6 (0 binary, 6 integer, 0 implicit integer, 0 continuous)
>    Constraints      : 1 initial, 1 maximal
> OBJECTIVE
>    Sense            : maximize
> VARIABLES
>    [integer] <x0>: obj=575.555555555556, original bounds=[0,10000000000]
>    [integer] <x1>: obj=1280.55555555556, original bounds=[0,10000000000]
>    [integer] <x2>: obj=1295, original bounds=[0,10000000000]
>    [integer] <x3>: obj=1295, original bounds=[0,10000000000]
>    [integer] <x4>: obj=1295, original bounds=[0,10000000000]
>    [integer] <x5>: obj=1295, original bounds=[0,10000000000]
> CONSTRAINTS
>    [linear] <R>: -10000000000 <=  +520<x0>[I] +1000<x1>[I] +1066<x2>[I]
> +1120<x3>[I] +1150<x4>[I] +1250<x5>[I] <= 5180;
> END
> #########################  SCIPprintTransProblem  ############################
> STATISTICS
>    Problem name     : t_Knapsack
>    Variables        : 6 (0 binary, 6 integer, 0 implicit integer, 0 continuous)
>    Constraints      : 1 initial, 1 maximal
> OBJECTIVE
>    Sense            : minimize
>    Scale            : 0.555555555555556
> VARIABLES
>    [integer] <t_x0>: obj=-1036, global bounds=[1,2], local bounds=[1,2]
>    [integer] <t_x1>: obj=-2305, global bounds=[3,3], local bounds=[3,3]
>    [integer] <t_x2>: obj=-2331, global bounds=[2,2], local bounds=[2,2]
>    [integer] <t_x3>: obj=-2331, global bounds=[-0,0], local bounds=[-0,0]
>    [integer] <t_x4>: obj=-2331, global bounds=[-0,0], local bounds=[-0,0]
>    [integer] <t_x5>: obj=-2331, global bounds=[-0,0], local bounds=[-0,0]
> CONSTRAINTS
>    [linear] <R>:  +625<t_x5>[I] +500<t_x1>[I] +533<t_x2>[I]
> +560<t_x3>[I] +575<t_x4>[I] +260<t_x0>[I] <= 2590;
> END
> #########################  SCIPprintSol  ############################
> objective value:                            -infinity
> x0                               -1.00125783520231e-015
> (obj:575.555555555556)
> x1                                                2.6   (obj:1280.55555555556)
> x2                                                  2   (obj:1295)
> x3                                  0.400000000000001   (obj:1295)
> x4                                                 -0   (obj:1295)
> x5                                                  0   (obj:1295)
>
> Thanks in advance,
> Luciana
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-- 
Jakob Witzig

Zuse Institute Berlin (ZIB)

Division Mathematical Optimization and Scientific Information
Research Group Mathematical Optimization Methods

Takustrasse 7
14195 Berlin

Tel. : +49 (0)30 84185-416
Fax  : +49 (0)30 84185-269
email: witzig at zib.de