图 书 馆
基于草图的最优分布子模优化Optimal Distributed Submodular Optimization via Sketching |
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| 课程网址: | http://videolectures.net/kdd2018_bateni_distributed_submodular/ |
| 主讲教师: | Mohammadhossein Bateni |
| 开课单位: | Google, Inc. |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present distributed algorithms for several classes of submodular optimization problems such as k-cover, set cover, facility location, and probabilistic coverage. The new algorithms enjoy almost optimal space complexity, optimal approximation guarantees, optimal communication complexity (and run in only four rounds of computation), addressing major shortcomings of prior work. We first present a distributed algorithm for k-cover using only O˜(n) space per machine, and then extend it to several submodular optimization problems, improving previous results for all the above problems—e.g., our algorithm for facility location problem improves the space of the best known algorithm (Lindgren et al. [26]). Our algorithms are implementable in various distributed frameworks such as MapReduce and RAM models. On the hardness side we demonstrate the limitations of uniform sampling via an information theoretic argument. Furthermore, we perform an extensive empirical study of our algorithms (implemented in MapReduce) on a variety of datasets. We observe that using sketches 30–600 times smaller than the input, one can solve the coverage maximization problem with quality very close to that of the state-of-the-art single-machine algorithm. Finally, we demonstrate an application of our algorithm in largescale feature selection. |
| 关 键 词: | 基于草图; 最优分布子模优化; k覆盖分布式算法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-09：cyh |
| 最后编审: | 2023-05-15：cyh |
| 阅读次数: | 33 |