[ https://issues.apache.org/jira/browse/MATH-828?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13418345#comment-13418345 ]
Alexey Slepov edited comment on MATH-828 at 7/19/12 3:09 PM:
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ApacheSimplexWrapperTest.java is the entry point
ApacheSimplexWrapper.java is the main class
commons-math3-3.0.jar is that same Apache Math 3 Lib I use
was (Author: alexeyslepov):
ApacheSimplexWrapperTest.java is the entry point
ApacheSimplexWrapper.java is the main class
commons-math3-3.0.jar is exactly the Apache Math 3 Lib I use
> Not expected UnboundedSolutionException
> ---------------------------------------
>
> Key: MATH-828
> URL: https://issues.apache.org/jira/browse/MATH-828
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 3.0
> Environment: Intel Core i5-2300 Windows XP SP3
> Reporter: Alexey Slepov
> Priority: Blocker
> Labels: linear, math, programming
> Fix For: 3.0
>
> Attachments: ApacheSimplexWrapper.java, ApacheSimplexWrapperTest.java, Entity.java, commons-math3-3.0.jar
>
>
> 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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