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New analysis details Sinkhorn-Knopp algorithm's local convergence

By PulseAugur Editorial · [2 sources] ·

Researchers have published a new analysis of the Sinkhorn-Knopp (SK) algorithm, focusing on its local convergence properties. The study provides the first nonasymptotic local analysis that matches existing asymptotic Jacobian-based rates, demonstrating that SK can be a polynomial-time algorithm for doubly stochastic matrix scaling under specific connectivity conditions. The work also introduces accelerated variants and improves the complexity for dense matrices. AI

IMPACT Provides a deeper theoretical understanding of matrix scaling algorithms, potentially impacting future AI model optimization techniques.

RANK_REASON The cluster contains a research paper detailing theoretical analysis of an algorithm.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New analysis details Sinkhorn-Knopp algorithm's local convergence

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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp

    We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its variants, its local linear convergence behavior remains less understood. We address this gap by providing the first nonasymptotic loca…

  2. arXiv stat.ML TIER_1 English(EN) · Wenzhi Gao, Zhaonan Qu, Yinyu Ye, Madeleine Odell ·

    Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp

    arXiv:2608.11760v1 Announce Type: cross Abstract: We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its variants, its local linear convergence behavior remains less understood. We address …

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