Decision Optimization

Decision Optimization

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BranchCallback with getLB(var) > getUB(var)

  • 1.  BranchCallback with getLB(var) > getUB(var)

    Posted Wed January 14, 2009 11:31 AM

    Originally posted by: SystemAdmin


    [bistra said:]

    I am writing a IloCplex::BranchCallbackI and I get to a node where I have an integer boolean variable that is set to an integer value but its current bounds are infeasible, i.e. getLB(var) > getUB(var)

    Is it safe to prune this node as infeasible?

    thank you,
    bistra
    #CPLEXOptimizers
    #DecisionOptimization


  • 2.  Re: BranchCallback with getLB(var) > getUB(var)

    Posted Fri January 16, 2009 08:23 AM

    Originally posted by: SystemAdmin


    [bistra said:]

    I found a similar topic posted some time back:
    "Inconsistent bounds while using a Branching Callback"

    [url=http://forums.ilog.com/optimization/index.php?topic=106.0]http://forums.ilog.com/optimization/index.php?topic=106.0[/url]

    However, the replies to this post just say that it is unclear what cplex has done in its own variable selection.
    I am using strong branching (variable selection 3) as the default branching selection (which is pretty strong) but sometimes I want to override the branch variable selected.

    I am still not clear whether it is safe to conclude that this node and any possible children of it are infeasible because of the current inconsistent bounds...
    #CPLEXOptimizers
    #DecisionOptimization


  • 3.  Re: BranchCallback with getLB(var) > getUB(var)

    Posted Fri January 16, 2009 02:25 PM

    Originally posted by: SystemAdmin


    [Sylvain said:]

    My experience is that it is not safe to prune such nodes.

    My understanding was that branching on variables which have a feasibility status "ImpliedFeasible" would generate these inconsistent bounds.

    I just think it is some sort of bug which does not affect the result of the optimization but makes life more difficult for the user who develops BranchCallbacks.
    #CPLEXOptimizers
    #DecisionOptimization