אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Motivated by machine learning applications, much research over the last decade was devoted to solving submodular maximization problems under Big Data computational models. Perhaps the most basic such problem is the problem of maximizing a submodular function subject to a cardinality constraint. A recent series of papers has studied this problem in the data stream model, and in particular, fully determined the approximation ratio that can be obtained for it by (semi-)-streaming algorithms both when the objective function is monotone and non-monotone. In this talk we will survey the main ideas behind these tight results. Based on joint works with Naor Alaluf, Alina Ene, Huy Nguyen, Ashkan Norouzi-Fard, Andrew Suh, Ola Svensson and Rico Zenklusen.