Near-Optimal Asymmetric Binary Matrix Partitions.

We study the asymmetric binary matrix partition problem that was recently introduced by Alon et al. (Proceedings of the 9th Conference on Web and Internet Economics (WINE), pp 1-14, 2013). Instances of the problem consist of an $$n \times m$$ binary matrix A and a probability distribution over its columns. A partition scheme $$B=(B_1,\ldots ,B_n)$$ consists of a partition $$B_i$$ for each row i of A. The partition $$B_i$$ acts as a smoothing operator on row i that distributes the expected value of each partition subset proportionally to all its entries. Given a scheme B that induces a smooth matrix $$A^B$$ , the partition value is the expected maximum column entry of $$A^B$$ . The objective is to find a partition scheme such that the resulting partition value is maximized. We present a 9/10-approximation algorithm for the case where the probability distribution is uniform and a $$(1-1/e)$$ -approximation algorithm for non-uniform distributions, significantly improving results of Alon et al. Although our first algorithm is combinatorial (and very simple), the analysis is based on linear programming and duality arguments. In our second result we exploit a nice relation of the problem to submodular welfare maximization. [ABSTRACT FROM AUTHOR]