LGPROct 25, 2019

Strong Log-Concavity Does Not Imply Log-Submodularity

arXiv:1910.11544v12 citations
Originality Incremental advance
AI Analysis

This addresses a theoretical gap in probability and combinatorics, clarifying properties of discrete distributions, but is incremental as it corrects a specific conjecture.

The paper disproves a conjecture that strong log-concavity implies log-submodularity for discrete distributions and their generating polynomials, showing it is false through counterexamples.

We disprove a recent conjecture regarding discrete distributions and their generating polynomials stating that strong log-concavity implies log-submodularity.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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