Presolve Yields sub-Optimal Result (Bug tied to GenConstraint?)

回答済み

Alexander Paskov

2022年12月31日 06:54 編集済み

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I'm solving the problem

 

I've gone through the forums and implemented it in Python as such

 

However, as shown in the following log, the pre-solve yields a sub-Optimal result. Is this possibly a bug related to the presolve? For reference, the optimal solution yields objective 0.363804 by setting x[1,0]=x[1,1]=1.

Any help would be greatly appreciated.

関連

• General Constraints
• Python

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  Marika Karbstein

  • Gurobi Staff

  2023年01月02日 14:43

  Did you check the model you created? You could write the LP file with model.write("model.lp"). What are the values for p and v/w? Can you post the lp file here? 

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  Alexander Paskov

  2023年01月02日 17:12 編集済み

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  Thank you for the response! I can't seem to upload the LP file here (only jpeg, gif, or png are allowed), so here is a screenshot of the model.lp file itself:

  The values are as follows:

  P = [0.15539925, 0.06792077

         0.39277593, 0.26142169]

  V = [0.16961872, 0.08829835]

  This is a different instance than the one posted above, but the result is still the same — the x variables are all set to 0 and Gurobi claims the "optimal objective" is 0.2579. However, doing it by hand, setting x[0,0]=x[1,1]=1 is clearly feasible and yields a better objective of 0.2084.

   

  EDIT-APPEND: For the gen constraint, I added "FuncPieces=-2 FuncPieceError=0.0001" requirement to ensure it's not an approximation error of the log, but the results didn't change.

   

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  Marika Karbstein

  • Gurobi Staff

  2023年01月03日 06:55

  For the variables 

  a = model.addVars(m, vtype=gp.GRB.CONTINUOUS, name='a')

  no lower bound is defined. The default lower bound is 0. Hence, the solution you mention is not feasible.

  a = model.addVars(m, vtype=gp.GRB.CONTINUOUS, lb=-1, name='a')

  will give you x[0,0]=x[1,1]=1 as optimal solution.

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