Decision Optimization

Decision Optimization

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


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  • 1.  about numerical trouble

    Posted 08/16/12 10:23 AM

    Originally posted by: SystemAdmin


    Hi, all,

    I have a MIP instance. With CPLEX default parameters, I can solve this instance within 0.28 seconds. But some binary varaibles have values such as 0.000001 and 0.999999.

    Then I tried to set MIP tolerance integrity to 0 and SIMPLEX tolerance feasibility to the minimum value, 1e-9. Now the solution course is significantly slowed down and the tree size is very large. After 1800 seconds, the GAP is still 26.32% and the tree size is 120 M. Finally CPLEX failed to solve this instance and terminated out of memory.

    Is there any to figure out such a numerical trouble?

    Thanks.

    Shaon
    #CPLEXOptimizers
    #DecisionOptimization


  • 2.  Re: about numerical trouble

    Posted 08/16/12 02:02 PM

    Originally posted by: EdKlotz


    > shaonli wrote:
    > Hi, all,
    >
    > I have a MIP instance. With CPLEX default parameters, I can solve this instance within 0.28 seconds. But some binary varaibles have values such as 0.000001 and 0.999999.
    >
    > Then I tried to set MIP tolerance integrity to 0 and SIMPLEX tolerance feasibility to the minimum value, 1e-9. Now the solution course is significantly slowed down and the tree size is very large. After 1800 seconds, the GAP is still 26.32% and the tree size is 120 M. Finally CPLEX failed to solve this instance and terminated out of memory.
    >
    > Is there any to figure out such a numerical trouble?
    >
    > Thanks.
    >
    > Shaon

    Take a look at http://www-01.ibm.com/support/docview.wss?uid=swg21399984
    for a detailed discussion of this. In a nutshell, examine
    the constraints containing integer variables that took values such
    as .000001 in the original run. They probably have have large coefficients.
    Try either reducing those coefficients if that doesn't compromise the
    meaning of your model, or replacing those constraints with indicator
    constraints if it does.
    #CPLEXOptimizers
    #DecisionOptimization