English(EN) Information Limits of Low-Rank Approximation Certification

新研究详细介绍了低秩近似认证的信息限制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员表征了认证矩阵低秩近似所关联的成本，并确定了单个近似矩阵候选的确切极小极大查询常数。他们的工作还解决了随着近似空间扩展而重复使用验证响应的问题，表明一个批次可以在不增加查询预算的情况下支持整个嵌套路径，而无需增加检查次数。该研究进一步比较了同一分散谱族上的两个统一有效证书，优化验证预算，从而在真实目标之外产生验证和构造的成本分别为 N^(1/3) 和 N^(2/3) 数量级。 AI

排序理由该集群包含一篇详细介绍数学研究的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 4 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍数学研究的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: Standard; On-topic for AI-industry coverage; kept in the public index.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Kang Liu, Bohao Qu · 2026-10-05 04:00

低秩近似认证的信息限制

arXiv:2610.03321v1 Announce Type: cross Abstract: Low-rank approximation can require additional matrix--vector products to verify that its error meets a prescribed tolerance. We characterize this certification cost for both relative matrix error and mean-square output error. For …

本文如何被排名

报道来源 [1]

低秩近似认证的信息限制

相关实体

相关话题