Fast submodular maximization subject to k-extendible system constraints

Teng Li, Hyo-Sang Shin, Antonios Tsourdos

As the scales of data sets expand rapidly in some application scenarios, increasing efforts have been made to develop fast submodular maximization algorithms. This paper presents a currently the most efficient algorithm for maximizing general non-negative submodular objective functions subject to $k$-extendible system constraints. Combining the sampling process and the decreasing threshold strategy, our algorithm Sample Decreasing Threshold Greedy Algorithm (SDTGA) obtains an expected approximation guarantee of ($p-\epsilon$) for monotone submodular functions and of ($p(1-p)-\epsilon$) for non-monotone cases with expected computational complexity of only $O(\frac{pn}{\epsilon}\ln\frac{r}{\epsilon})$, where $r$ is the largest size of the feasible solutions, $0

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