Decision Optimization

Decision Optimization

Delivers prescriptive analytics capabilities and decision intelligence to improve decision-making.


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  • 1.  (C API) branch on multiple children

    Posted 06/24/12 04:01 PM

    Originally posted by: NSopt


    Hello.
    I'm working on branching study by using C callable API.
    I saw below answer in previous discussed thread.

    " Not necessarily fatal if you're willing to do a bit of extra work. Suppose you want to split node A into three children (B, C and D). At node A you call the branch callback twice. The first time you add a combination of constraints and bounds that defines B. The second time you add some single constraint or bound, however vacuous, that applies to both C and D, creating child A'. Then at A' you split into C and D. It requires a bit of extra bookkeeping to keep track of which nodes need which new pieces, but it's not horrible. CPLEX allows you attach a user data object to each node, so you can use a custom object to tag each node with information about whether (and how) it needs further splitting." by Paul Rubin

    Could Paul or anyone please explain more about this? (i.e., branch on more than two children)
    Especially, I'd like to know how we can attach a user data object to each node and how we can use attached information for this implementation.
    Thank you.

    Best,
    NS
    #CPLEXOptimizers
    #DecisionOptimization


  • 2.  Re: (C API) branch on multiple children

    Posted 06/25/12 04:18 PM

    Originally posted by: SystemAdmin


    I wrote a blog post about it, including sample Java code. The C API is mechanically different, but you should be able to make the transition.

    Paul

    Mathematicians are like Frenchmen: whenever you say something to them, they translate it into their own language, and at once it is something entirely different. (Goethe)
    #CPLEXOptimizers
    #DecisionOptimization


  • 3.  Re: (C API) branch on multiple children

    Posted 06/26/12 02:44 PM

    Originally posted by: NSopt


    Thank you very much for your really good answer!
    It was a great help for me.
    Sincerely
    NS
    #CPLEXOptimizers
    #DecisionOptimization