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

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.
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