Unbounded solution when I put a lower bound other than 0.

Arif Ahmed

2019年06月17日 05:01

• 
• 
• 

Hi,

 

I am working on a MILP problem. I have a continuous variable x. I have an upperbound on the variable of around 450000 and a lower bound of 0. The problem solves nice and well. As soon as I change the lower bound to any number above 0 I get the following result:

Optimize a model with 2707 rows, 2982 columns and 12730 nonzeros
Model has 840 quadratic objective terms
Model has 7 quadratic constraints
Variable types: 840 continuous, 2142 integer (2142 binary)
Coefficient statistics:
Matrix range [1e+00, 5e+07]
QMatrix range [1e+00, 1e+00]
Objective range [0e+00, 0e+00]
QObjective range [2e+00, 2e+00]
Bounds range [1e+00, 5e+05]
RHS range [1e+00, 2e+06]
QRHS range [9e+05, 2e+06]
Presolve removed 7 rows and 28 columns
Presolve time: 0.00s

Explored 0 nodes (0 simplex iterations) in 0.00 seconds
Thread count was 1 (of 32 available processors)

Solution count 0

Model is infeasible
Best objective -, best bound -, gap -

 

 

Could anybody kindly assist please.

0

• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2024年12月06日 00:58

  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?.
• 

  Silke Horn

  • Gurobi Staff

  2019年06月17日 11:12

  This log says that your model is infeasible (not unbounded). Does your variable x take the value 0 in the solution of the model where the lower bound for x is 0? This would then probably mean that there is no feasible solution with x > 0.

  0

投稿コメントは受け付けていません。