Finding all feasible solutions

回答済み

Akram Kh

• 2021年08月11日 04:23

I need to find all feasible solutions of my milp model no matter if they are optimal or not. I have used the poolsolutions for this with the parameter "PoolSolutions =  2000000000" (max possible value). However, when I try this model with PoolSearchMode=2, I get different number of solutions in comparison with optimizing the same model with PoolSearchMode=1.

I suppose that solving a model with PoolSearchMode=2 should return the same number of solutions as solving the model with PoolSearchMode=1, as far as PoolGap equals infinity (its default value as in my case). Am I correct? If yes, what is the reason of the above observation?

I do appreciate any guidance in advance!

1

• 正式なコメント

  

  Simranjit Kaur

  Gurobi Staff

  • 2024年12月06日 00:28

  This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum. Or why not try our AI Gurobot?.
• 

  Tobias Achterberg

  Gurobi Staff

  • 2021年08月11日 13:01
  • 編集済み

  No, you need to use PoolSearchMode=2 to really find all feasible solutions. With PoolSearchMode=1 there is no guarantee of exhaustiveness. For example, PoolSearchMode=1 would not disable dual presolve reductions. Those reductions allow to discard optimal solutions if one can prove that at least one optimal solution remains feasible in the presolved model.

  I agree, however, that our documentation of the PoolSearchMode parameter is not 100% clear on this.

  You could check whether

   PoolSearchMode=2

  is equivalent to

  PoolSearchMode=1 DualReductions=0.

  From source code inspection, I think that disabling dual reductions is really the only relevant difference.

  Regards,

  Tobias

   

  1
• 

  Akram Kh

  • 2021年08月12日 07:11

  

  

  

  Dear Tobias,

  Thanks for your reply. That was a great help for me.

  Regards,

  Akram

  0

投稿コメントは受け付けていません。