I have a binary MIP model to be solved through Gurobi's BB-tree. However, I will constantly add continuous variables x_c^(k) and new constraints Ax_c^(k) <= b^(k) into the model. During the process, the binary variables x_b will be given at the beginning and will not have new binary variables. Finally, the continuous variable will be x_c^(k), k = 1,2,....,N and the corresponding constraints are Ax_c^(k) <= b^(k), k = 1,2,....,N.
My final target is to minimize a function which only depends on the binary variables x_b.
The specific property of my model is that, if a solution (x_b) is infeasible after adding x_c^(k) and Ax_c^(k) <= b^(k), k = 1,2,....,m, then I know for sure that such a solution is infeasible when new continuous variables and constraints are added.
For each newly added variables and constraints, I need to solve the model and find the optimal x_b once. From my understanding, if I can inherit the B&B tree from previous optimization process, the information about which nodes should be pruned can also be inherited.
Is there any way to achieve this goal for acceleration? Thanks.
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