Variational Optimization for the Submodular Maximum Coverage Problem

We provide the first While [25] shows the greedy method with a modular approximation variational approximation for this problem based on the Nemhauser has good performance, we take a step further to build a mathematical divergence, and show that it can be solved efficiently using variational connection between the variational modular approximation optimization. The algorithm alternates between two steps: to a submodular function based on Namhauser divergence and (1) an E step that estimates a variational parameter to maximize a classical variational approximation based on KullbackâĂŞLeibler parameterized modular lower bound; and (2) an M step that updates divergence. We take advantage of this framework to iteratively solve the solution by solving the local approximate problem. We provide SMCP, leading to a novel variational approach. Analogous to the theoretical analysis on the performance of the proposed approach counterpart of variational optimization based on Kullback-Leibler and its curvature-dependent approximate factor, and empirically divergence, the proposed method consists of two alternating steps, evaluate it on a number of public data sets and several application namely estimation (E step) and maximization (M step) to monotonically tasks.