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Describe the bug
Branching algorithm adds constraints for variable bounds (in function generate_children), but the bounds of the variable itself are not updated. The variable bounds retrieved from BlockDecomposition.callback_lb(cbdata, var) and BlockDecomposition.callback_ub(cbdata, var) are incorrect.
To Reproduce
Define a model with a pricing callback and maxnumnodes = 2
Optimize the model
Check the branching constraint (variable and rhs)
Update lb and ub in the pricing callback for the subproblem in which variable is, using BD.callback_lb(cbdata, variable) and BD.callback_ub(cbdata, variable)
Check last values of lb and ub:
If the branching constraint is greater or equal:
@test lb == rhs
If the branching constraint is less or equal:
@test ub == rhs
Expected behavior
Test will fail. The bound retrieved is always a perene bound.
Suggestion setcurlb! and setcurub! in generate_children.
The text was updated successfully, but these errors were encountered:
Describe the bug
Branching algorithm adds constraints for variable bounds (in function
generate_children
), but the bounds of the variable itself are not updated. The variable bounds retrieved fromBlockDecomposition.callback_lb(cbdata, var)
andBlockDecomposition.callback_ub(cbdata, var)
are incorrect.To Reproduce
maxnumnodes = 2
lb
andub
in the pricing callback for the subproblem in whichvariable
is, usingBD.callback_lb(cbdata, variable)
andBD.callback_ub(cbdata, variable)
lb
andub
:If the branching constraint is greater or equal:
Expected behavior
Test will fail. The bound retrieved is always a perene bound.
Suggestion
setcurlb!
andsetcurub!
ingenerate_children
.The text was updated successfully, but these errors were encountered: