  The Maximum Balanced Biclique Problem (MBBP) is a prominent model with
numerous applications. Yet, the problem is NP-hard and thus computationally
challenging. We propose novel ideas for designing effective exact algorithms
for MBBP. Firstly, we introduce an Upper Bound Propagation procedure to
pre-compute an upper bound involving each vertex. Then we extend an existing
branch-and-bound algorithm by integrating the pre-computed upper bounds. We
also present a set of new valid inequalities induced from the upper bounds to
tighten an existing mathematical formulation for MBBP. Lastly, we investigate
another exact algorithm scheme which enumerates a subset of balanced bicliques
based on our upper bounds. Experiments show that compared to existing
approaches, the proposed algorithms and formulations are more efficient in
solving a set of random graphs and large real-life instances.
