Re: [ojAlgo-user] bug report: ILP problem

[Jeff S. <jef...@gm...>]

Thanks for looking into this.  I dont require anywhere near 16 digit
accuracy on x1.  In fact, I'm really only interested in the values of the
integral variables.  On the test case i sent you, I added the following:

model.options.problem = NumberContext.getGeneral(9);

and I get this output which is "correct".

Obj val: 4849.999999999305
Optimal: true
All vars:
x1 = 4.85E+3
x2013 = 1245
x2014 = 1269
x2015 = 1307

The "Optimal: true" output comes from:
res.getState().isOptimal()

which doesn't seem to coincide with your results...how are you determining
that you get a FEASIBLE, rather than OPTIMAL result?



On Thu, Apr 11, 2013 at 5:57 AM, Anders Peterson <an...@op...>wrote:

> I've run this test case, and I've created another using the model
> description from your first message.
>
> By setting
>
> tmpModel.options.problem = NumberContext.getGeneral(12);
>
> I can get correct (but maybe not accurate enough) solutions to both models:
>
> OPTIMAL 4199.999999999861 @ { 4199.999999999861, 1891.9999999999998,
> 1929.0 }
> FEASIBLE 4849.999999999305 @ { 4849.999999999305, 1244.9999999999998,
> 1269.0000000000002, 1307.0 }
>
>
> With the first model description you sent in you stated the correct
> solution to be:
> x1 = 4200.000000000075
> x2013 = 1892
> x2014 = 1929
>
> Do you really require full 16 digit accuracy on x1?
>
>
> With the second model something does go wrong with the linear solver in
> one of the integer solver's nodes (subproblems). That's why the result has
> state FEASIBLE rather than OPTIMAL. The fact that the returned feasible
> solution actually happens to be the correct/optimal solution is a
> coincidence. That problem with the linear solver is a bug - it somehow
> returns an infeasible solution. I'll look in to this further.
>
>
> Using NumberContext.getGeneral(12); as the default value for
> tmpModel.options.problem breaks a number of other test cases...
>
>
> /Anders
>
>
> On 8 apr 2013, at 18:02, Jeff Sherman <jef...@gm...> wrote:
>
> > Anders,
> > Please disregard the previous attachment and run this one.  Also, on one
> run i got these results:
> > Obj val: 5250.000000000088
> > Optimal: true
> > All vars:
> > x1 = 5.25E+3
> > x2013 = 1244
> > x2014 = 1269
> > x2015 = 1308
> >
> >
> > On Mon, Apr 8, 2013 at 11:52 AM, Jeff Sherman <jef...@gm...>
> wrote:
> > Andres,
> > Thanks for your response.  The attached code  produces the following
> results:
> > x1 = 0
> > x2013 = 1245
> > x2014 = 1269.9
> > x2015 = 1306.1
> >
> > Note that x2014 and x2015 are set as integral variables.
> >
> > If in the calls to model.addExpression, i change the strings that are
> passed in, I can also obtain the following results:
> > x1 = 2890.64039409
> > x2013 = 1244
> > x2014 = 1269.45812808
> > x2015 = 1307.54187192
> >
> > I did some looking into the code and I noticed in the
> ExpressionsBasedModel class you have:
> >
> > private final HashMap<String, Expression> myExpressions = new
> HashMap<String, Expression>();
> >
> > and at points, you get a collection of myExpression.values(), the order
> of which is not guaranteed.  I'm not sure if you care about this ordering
> but i thought id point it out in case.
> >
> > The correct results for the problem are:
> > x1 = 4849.999999997941
> > x2013 = 1245
> > x2014 = 1269
> > x2015 = 1307
> >
> > Please let me know if I can provide any additional information or
> clarification.
> >
> > Best,
> > Jeff
> >
> >
> >
> > On Mon, Apr 8, 2013 at 5:22 AM, Anders Peterson <an...@op...>
> wrote:
> > ...and please experiment with different values on the various
> model/solver options - in particular tmpYourModel.options..problem
> >
> > /Anders
> >
> >
> > On 8 apr 2013, at 08:58, Anders Peterson <an...@op...> wrote:
> >
> > > It's better you send me source code that I can execute and use as a
> test case. /Anders
> > >
> > > On 8 apr 2013, at 01:25, Jeff Sherman <jef...@gm...> wrote:
> > >
> > >> When I execute the following program:
> > >> Minimize
> > >> 1*x1
> > >> Subject To
> > >> 1*x2013 >= 0
> > >> 1*x2014 >= 0
> > >> 1*x1 >= 0
> > >> -5000.0*x2014 + 5100.0*x2013 + -1.0*x1 <= 0
> > >> -5000.0*x2014 + 5100.0*x2013 + 1.0*x1 >= 0
> > >> 5000*x2013 + 5000*x2014 = 19105000
> > >> Bounds
> > >> Variables
> > >> x2014 integer
> > >> x2013 integer
> > >> x1 continuous
> > >>
> > >> I get these results which are suboptimal:
> > >> x1 = 5899.999999996906
> > >> x2013 =  1891
> > >> x2014 = 1930
> > >>
> > >> Or these results (which seem to occur at random):
> > >> x1 = Infinity
> > >> x2013 =  0
> > >> x2014 = 0
> > >>
> > >> Using another solver, I obtain:
> > >> x1 = 4200.000000000075
> > >> x2013 = 1892
> > >> x2014 = 1929
> > >>
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> <TestILP.java>------------------------------------------------------------------------------
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