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Poster
Fully Dynamic Algorithm for Constrained Submodular Optimization
Silvio Lattanzi · Slobodan Mitrović · Ashkan Norouzi-Fard · Jakub Tarnawski · Morteza Zadimoghaddam

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1812
in Poster Session 7 »
The task of maximizing a monotone submodular function under a cardinality constraint is at the core of many machine learning and data mining applications, including data summarization, sparse regression and coverage problems. We study this classic problem in the fully dynamic setting, where elements can be both inserted and removed. Our main result is a randomized algorithm that maintains an efficient data structure with a poly-logarithmic amortized update time and yields a $(1/2-epsilon)$-approximate solution. We complement our theoretical analysis with an empirical study of the performance of our algorithm.
Keywords: Learning Theory · Frequentist Statistics · Theory · Theory; Theory

Author Information

Silvio Lattanzi (Google Research)
Slobodan Mitrović (MIT)
Ashkan Norouzi-Fard (Google Research)
Jakub Tarnawski (Microsoft Research)
Morteza Zadimoghaddam (Google Research)

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