Gurobi solution count 0

Answered

Juri Zahn

June 16, 2024 07:28

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Dear users,

Recently, I started using the computer cluster at my university. I have an optimization program written in Julia that contains a double for loop. The first loop determines the instance (1:4), and the second loop handles the scenario (1:15). When running my program, I get an error in my log file indicating that my model is infeasible or unbounded, as my solution count is 0. The issue is that I know there is a solution because I have run my program successfully on my own computer. When I set the scenario to a certain value like 14, I get a solution for all instances. Does anyone have any ideas about where my mistake might be or how I can fix it?

Thank you and best regards,

Juri

 

 

 

Explored 1340455 nodes (2138209912 simplex iterations) in 7200.56 seconds (14977.01 work units)
Thread count was 64 (of 64 available processors)

Solution count 0

Time limit reached
Best objective -, best bound 1.509507345475e+05, gap -

User-callback calls 4802838, time in user-callback 1.17 sec
ERROR: LoadError: Result index of attribute MathOptInterface.VariablePrimal(1) out of bounds. There are currently 0 solution(s) in the model.
Stacktrace:

0

• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  November 13, 2026 01:23

  This post is more than two 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 try Gurobot, our chatbot interface offering instant, expert-level support.
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  Jaromił Najman

  • Gurobi Staff

  June 17, 2024 08:21

  Hi Juri,

  You should extract the infeasible model and then try the approaches described in How do I determine why my model is infeasible? Please note that you might need to set the InfUnbdInfo.

  If the issue is about finding feasible solution then you might want to experiment with the NoRelHeurTime parameter which executes the so-called No-relaxation heuristic. This heuristic is run before the B&B algorithm and its goal is to find feasible solutions. Other than that, you could experiment with the Heuristics and MIPFocus parameters. You could also have a look at the Most important parameters for MIPs.

  Best regards, 
  Jaromił

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