arXiv Machine Learning By Andrea Pietracaprina, Geppino Pucci, Stefano Zanon

Streaming algorithms for robust max-min diversification

Read the original on arXiv Machine Learning →

arXiv:2610. 01456v1 Announce Type: new Abstract: Given a set of $n$ points $X$ in a metric space and an integer $k$, max-min diversification aims to select $k$ points of $X$ maximizing their minimum pairwise distance.

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arXiv Machine Learning
6d ago

Consistent Submodular Maximization with Full Foresight

The paper studies online algorithms for monotone submodular maximization with full knowledge of the future and unlimited computation. It establishes optimal approximation thresholds of 3/4 for deterministic algorithms and approximately 0.7942 for randomized algorithms, while allowing a constant number of changes per step. The authors provide matching impossibility results and achieve these bounds by balancing preparation for future arrivals with protection of the current solution, using techniques such as interval-selection analysis for randomized algorithms and certificate-based rounding for deterministic ones.

By Eric Balkanski, Paul D\"utting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson, Morteza Zadimoghaddam