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New research defines optimal thresholds for consistent submodular maximization

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "Consistent Submodular Maximization with Full Foresight" explores the challenges of online algorithms with limited recourse. The study focuses on monotone submodular maximization, where algorithms must maintain a solution of up to k elements with a limited number of changes. Researchers have established optimal approximation thresholds of 3/4 for deterministic algorithms and approximately 0.7942 for randomized algorithms, proving matching impossibility results for coverage functions. AI

RANK_REASON The cluster contains a single academic paper published on arXiv detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=0.4]

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New research defines optimal thresholds for consistent submodular maximization

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The cluster contains a single academic paper published on arXiv detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Eric Balkanski, Paul D\"utting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson, Morteza Zadimoghaddam ·

    Consistent Submodular Maximization with Full Foresight

    arXiv:2611.06223v1 Announce Type: cross Abstract: Online algorithms with bounded recourse face three intertwined obstacles: uncertainty about the future, computational limits, and a limited budget for changes. We ask a fundamental question that separates consistency from the othe…

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