Submodular Maximization via Taylor Series ApproximationDownload PDFOpen Website

2021 (modified: 26 Jun 2021)CoRR 2021Readers: Everyone
Abstract: We study submodular maximization problems with matroid constraints, in particular, problems where the objective can be expressed via compositions of analytic and multilinear functions. We show that for functions of this form, the so-called continuous greedy algorithm attains a ratio arbitrarily close to $(1-1/e) \approx 0.63$ using a deterministic estimation via Taylor series approximation. This drastically reduces execution time over prior art that uses sampling.
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