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Streaming Algorithms for Maximizing Monotone Submodular Functions
报告人:刘彬教授,中国海洋大学 时间:2021年12月30日19:00 字号:

腾讯会议:614141714

邀请人:张晓岩教授

摘 要:Submodular functions play a key role in combinatorial optimization field. The general problem of optimizing a submodular function subject to different constraints captures many problems of interest both in theory and in practice, including maximum coverage, social welfare maximization, influence maximization in social networks, sensor placement, maximum cut, and facility location, etc. On the other hand, in the current big data environment, the input data of many applications is much larger than the storage capacity of individual computer. In this case we need to process data by using streaming model. In this talk, I will show several basic results and methods in this area, and discuss some follow-up studies in recent years.

报告人简介:刘彬,中国海洋大学数学科学学院教授、院长助理。2010年毕业于山东大学运筹学与控制论专业,获理学博士学位。2016年作为访问学者赴美国德克萨斯大学达拉斯分校访问一年。研究领域和兴趣包括:次模优化、近似算法的设计与分析、图论等。在Journal of Global OptimizationJournal of Graph Theory Journal of Combinatorial Optimization等期刊和INFOCOM等会议发表论文40余篇,先后主持国家自然科学基金等科研项目共8项。目前担任中国工业与应用数学学会副秘书长、信息和通讯技术领域的数学专委会委员;中国运筹学会图论组合分会青年理事和副秘书长、数学规划分会青年理事等;美国数学会Mathematical Reviews评论员等。


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