Decision Optimization

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CPLEX Error 5002: 'q1' is not convex

  • 1.  CPLEX Error 5002: 'q1' is not convex

    Posted Mon March 14, 2016 09:50 AM

    Originally posted by: NguyenHuuTRi


    Hi,

    I encounter the error :CPLEX Error  5002: 'q1' is not convex when I am running my model. I can not identify which error is and how can i deal with this problem with my model. I have attached my model here and hope to receive your help.

     

    Thanks so much.

     

    Tri.

     


    #DecisionOptimization
    #OPLusingCPLEXOptimizer


  • 2.  Re: CPLEX Error 5002: 'q1' is not convex

    Posted Wed March 16, 2016 03:56 AM

    Hi

    which cplex version do you use ?

    regards


    #DecisionOptimization
    #OPLusingCPLEXOptimizer


  • 3.  Re: CPLEX Error 5002: 'q1' is not convex

    Posted Wed March 16, 2016 04:51 AM

    By the way you could try Constraint Programming:

    just add:

    using CP;

    at the beginning of your model.

    regards


    #DecisionOptimization
    #OPLusingCPLEXOptimizer


  • 4.  Re: CPLEX Error 5002: 'q1' is not convex

    Posted Wed March 16, 2016 10:51 AM

    Originally posted by: NguyenHuuTRi


    Hi,

     

    I am using CPLEX 12.6.2. My question is that my model is using linear programming in CPLEX and my model has two objectives (one is linear and another one is nonlinear), so I wonder if I could use CP or not as your suggestion.

     

    Thanks.

     

     


    #DecisionOptimization
    #OPLusingCPLEXOptimizer


  • 5.  Re: CPLEX Error 5002: 'q1' is not convex

    Posted Fri March 18, 2016 10:58 AM

    yes and you should have a look at

    IDE and OPL > Optimization Programming Language (OPL) > Language User's Manual > The application areas > Applications of constraint programming

    Solving models with a multi-criteria objective

    CP Optimizer can solve models with a multi-criteria objective.

    The truckfleet example illustrates this feature. The model and data files can be found in:

    Install_dir\opl\examples\opl\truckfleet

     

    regards

     


    #DecisionOptimization
    #OPLusingCPLEXOptimizer


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