Improving the LP bound of a MILP by branching concurrently

We’ll measure the differences of the dual variables and the gain of the objective function when creating new problems, which each has one inequality more than the starting LP-instance. These differences of the dual variables are naturally connected to the branches. Then we’ll choose those differences of dual variables, so that for all combinations of choices at the connected branches, all dual inequalities will hold for sure. By adding the gain of each chosen branching, we get a total gain, which gives a better limit of the original problem. By this technique it is also possible to create cuts.