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We'll measure the differences of the dual variables and the gain of theobjective function when creating new problems, which each has one inequalitymore than the starting LP-instance. These differences of the dual variables arenaturally connected to the branches. Then we'll choose those differences ofdual variables, so that for all combinations of choices at the connectedbranches, all dual inequalities will hold for sure. By adding the gain of eachchosen branching, we get a total gain, which gives a better limit of theoriginal problem. By this technique it is also possible to create cuts.

Improving the LP bound of a MILP by branching concurrently