How does Gurobi determine whether a MIQCQP problem is feasible or not

Answered

Sixiong You

• March 05, 2020 21:04

Hi, I am working on the mixed-integer quadratically constrained quadratic programmings (MIQCQP), and I found that when I randomly generated some nonconvex MIQCQP problems, the Gurobi can tell me quickly if this problem is infeasible or not, I am wondering why Gurobi can be so efficient to judge whether a problem is feasible or not, or is it possible gives me some wrong information? 

Thanks

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• Official comment

  

  Simranjit Kaur

  Gurobi Staff

  • December 06, 2024 00:49

  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?.
• 

  Yuriy Zinchenko

  Gurobi Staff

  • March 06, 2020 00:44

  Generally speaking, Gurobi will apply same "branch and cut" approach as for the MIP, e.g., see here for very basic explanation, https://www.gurobi.com/resource/mip-basics/

  It sounds we are just very efficient on your models :)

   

  Hope this helps,

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  Sixiong You

  • March 06, 2020 21:19

  

  

  

  Thanks for your fast reply! I have read the recommended website, and it is helpful. 

  And I have one more question, just for the mixed-binary quadratic constrained programming (maybe nonconvex), is it possible that GUROBI will take a long time to tell whether this model is feasible or not? 

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  Yuriy Zinchenko

  Gurobi Staff

  • March 09, 2020 16:00

  It is certainly possible that the solver takes a while -- non-convex MIQP can be very challenging.

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