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From "Thomas Neidhart (JIRA)" <>
Subject [jira] [Reopened] (MATH-828) Not expected UnboundedSolutionException
Date Fri, 20 Jul 2012 19:37:34 GMT


Thomas Neidhart reopened MATH-828:

Hi Alexey,

you are right, I was too quick to draw a conclusion, the way you setup the problem is indeed

What I have seen is that you use a very small maxUlps setting in your solver. The default
it 10 and should work better atm. I will further look into it, it seems to be related to numerical

Solving the same problems with glpk seems to be more robust, which maybe due to the scaling
that is applied there to improve numerical properties of the constraint matrix.
> Not expected UnboundedSolutionException
> ---------------------------------------
>                 Key: MATH-828
>                 URL:
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: 3.0
>         Environment: Intel Core i5-2300 Windows XP SP3
>            Reporter: Alexey Slepov
>              Labels: linear, math, programming
>             Fix For: 3.0
>         Attachments:,,,
> SimplexSolver throws UnboundedSolutionException when trying to solve minimization linear
programming problem. The number of exception thrown depends on the number of variables.
> In order to see that behavior of SimplexSolver first try to run JUnit test setting a
final variable ENTITIES_COUNT = 2 and that will give almost good result and then set it to
15 and you'll get a massive of unbounded exceptions.
> First iteration is runned with predefined set of input data with which the Solver gives
back an appropriate result.
> The problem itself is well tested by it's authors (mathematicians who I believe know
what they developed) using Matlab 10 with no unbounded solutions on the same rules of creatnig
random variables values.
> What is strange to me is the dependence of the number of UnboundedSolutionException exceptions
on the number of variables in the problem.
> The problem is formulated as
> min(1*t + 0*L) (for every r-th subject)
> s.t.
> -q(r) + QL >= 0
> x(r)t - XL >= 0
> L >= 0
> where 
> r = 1..R, 
> L = {l(1), l(2), ..., l(R)} (vector of R rows and 1 column),
> Q - coefficients matrix MxR
> X - coefficients matrix NxR 

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