For illustration we have applied our approach to the unate covering problem.
Raiser is invoked at a node of the search tree which is deep enough to justify negative thinking.
Raiser tries to detect a hard core of the matrix corresponding to the node by augmenting an independent set of rows in order to increase incrementally the cost of the minimum solutions covering the matrix. Experiments show that our program, aura, outperforms both espresso and our enhancement of espresso using Coudert's limit lower bound. It is always faster and in the most difficult examples either has a running time better by up to two orders of magnitude, or the other programs fail to finish due to timeout or spaceout. The package scherzo is faster on some examples and loses on others, due to a less powerful pruning strategy of the search space, partially mitigated by a more effective computation of the maximal independent set. Inc. All rights reserved.