If no heuristic subroutine is available for generating feasible solutions quickly, then a unique two-phase algorithm can also be invoked. In the two-phase method, the algorithm is first run to completion on a specified set of core variables. Any node that would have been pruned in the first phase is instead sent to a pool of candidates for the second phase. If the set of core variables is small, but well-chosen, this first phase should be finished quickly and should result in a near-optimal solution. In addition, the first phase will produce a list of useful cuts. Using the upper bound and the list of cuts from the first phase, the root node is repriced--that is, it is reprocessed with the full set of variables and cuts. The hope is that most or all of the variables not included in the first phase will be priced out of the problem in the new root node. Any variable thus priced out can be eliminated from the problem globally. If we are successful at pricing out all of the inactive variables, we have shown that the solution from the first phase was, in fact, optimal. If not, we must go back and price out the (reduced) set of extra variables in each leaf of the search tree produced during the first phase. We then continue processing any node in which we fail to price out all the variables.
In order to avoid pricing variables in every leaf of the tree, we can trim the tree before the start of the second phase. Trimming the tree consists of eliminating the children of any node for which each child has lower bound above the current upper bound. We then reprocess the parent node itself. This is typically more efficient, since there is a high probability that, given the new upper bound and cuts, we will be able to prune the parent node and avoid the task of processing each child individually.