We present the first mini-batch algorithm for maximizing a non-negative monotone decomposable submodular function, $F=\sum_{i=1}^N f^i$, under a set of constraints. We improve over the sparsifier based approach both in theory and in practice. We experimentally observe that our algorithm generates solutions that are far superior to those generated by the sparsifier based approach.

翻译：我们提出了首个用于在约束条件下最大化非负单调可分解子模函数 $F=\sum_{i=1}^N f^i$ 的小批量算法。该算法在理论和实践上均优于基于稀疏化的方法。实验观察表明，我们的算法生成的解远优于基于稀疏化方法生成的解。