  The reliable fraction of information is an attractive score for quantifying
(functional) dependencies in high-dimensional data. In this paper, we
systematically explore the algorithmic implications of using this measure for
optimization. We show that the problem is NP-hard, which justifies the usage of
worst-case exponential-time as well as heuristic search methods. We then
substantially improve the practical performance for both optimization styles by
deriving a novel admissible bounding function that has an unbounded potential
for additional pruning over the previously proposed one. Finally, we
empirically investigate the approximation ratio of the greedy algorithm and
show that it produces highly competitive results in a fraction of time needed
for complete branch-and-bound style search.
