The best known bound on the optimal objective. When solving a MIP model, the algorithm maintains both a lower bound and an upper bound on the optimal objective value. For a minimization model, the upper bound is the objective of the best known feasible solution, while the lower bound gives a bound on the best possible objective.
In contrast to ObjBound, this attribute
does not take advantage of objective integrality information to round
to a tighter bound. For example, if the objective is known to take an
integral value and the current best bound is 1.5,
ObjBound will return 2.0 while
For examples of how to query or modify attributes, refer to our Attribute Examples.