Research map / Learning theory and algorithms / Submodular and game algorithms
When Streaming Fails: Dynamic Algorithms for Unconstrained Submodular Maximization
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The paper studies dynamic algorithms for unconstrained submodular maximization, asking whether dynamic settings can beat streaming-based limits as streaming and dynamic settings are commonly linked.
Problem
Dynamic unconstrained (non-monotone) submodular maximization, where elements are inserted and deleted; prior work linked dynamic and streaming settings in both algorithms and lower bounds.
Approach
Asks whether dynamic algorithms can outperform streaming-based approaches for unconstrained submodular maximization, given that streaming lower bounds have been transferred to the dynamic setting; the abstract indicates a negative/contrasting answer is in focus but full results are not available.
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- Do Less, Get More: Streaming Submodular Maximization with Subsampling (NeurIPS 2018 · similarity 0.93)
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