Name
Abstract | ||
|---|---|---|
In this paper, we focus on the continuous DR-submodular maximization over a network. By using the gradient tracking technique, two decentralized algorithms are proposed for deterministic and stochastic settings, respectively. The proposed methods attain the $epsilon$-accuracy tight approximation ratio for monotone continuous DR-submodular functions in only $O(1/epsilon)$ and $tilde{O}(1/epsilon)$ rounds of communication, respectively, which are superior to the state-of-the-art. Our numerical results show that the proposed methods outperform existing decentralized methods in terms of both computation and communication complexity. |
Year | Venue | Field |
|---|---|---|
2019 | international conference on artificial intelligence and statistics | Mathematical optimization,Computer science,Submodular set function,Communication complexity,Submodular maximization,Tilde,Monotone polygon,Computation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiahao Xie | 1 | 0 | 2.03 |
| Chao Zhang | 2 | 8 | 5.49 |
| Zebang Shen | 3 | 17 | 9.36 |
| Chao Mi | 4 | 8 | 1.87 |
| Hui Qian | 5 | 59 | 13.26 |