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From "Alexey Slepov (JIRA)" <>
Subject [jira] [Commented] (MATH-828) Not expected UnboundedSolutionException
Date Mon, 23 Jul 2012 09:12:34 GMT


Alexey Slepov commented on MATH-828:

Thank you Thomas for reopenning and your confirmation that I use math3 properly. I'll play
around with maxUIps setting and will try GLPK that I've heard about but haven't tryed yet.

Gilles am I right thinking that I need to use SVN to get 3.1 (r12317576)
svn checkout commons-math3
or if I'm wrong please refer me to the proper link.
> 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: 3.1
>         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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