Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice

In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most are specified in a set function. Meanwhile, advancements have been studied for diminishing return (DR-submodular) function on integer lattice. Because plenty publications show that DR-submodular wide applications optimization problems such as sensor placement impose problems, optimal budget allocation, social network, and especially machine learning. this research, we propose two main streaming algorithms problem monotone under cardinality constraints. Our algorithms, which called StrDRS1 StrDRS2, (1/2−ϵ), (1−1/e−ϵ) approximation ratios O(nϵlog(logBϵ)logk), O(nϵlogB), respectively. We conducted several experiments to investigate performance our based allocation over bipartite influence model, an instance maximization The experimental results indicate proposed not only provide solutions with high value objective function, but also outperform state-of-the-art terms both number queries running time.