惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

雷峰网
雷峰网
博客园 - 叶小钗
博客园_首页
阮一峰的网络日志
阮一峰的网络日志
D
Docker
J
Java Code Geeks
B
Blog
G
Google Developers Blog
小众软件
小众软件
博客园 - 聂微东
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
IT之家
IT之家
量子位
WordPress大学
WordPress大学
美团技术团队
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
宝玉的分享
宝玉的分享
腾讯CDC
Martin Fowler
Martin Fowler
V
Visual Studio Blog
D
DataBreaches.Net
Stack Overflow Blog
Stack Overflow Blog
C
Check Point Blog

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
On Layer-Rainbow Latin Cubes Containing Layer-Rainbow Lat...
[Submitted on 14 Sep 2022 (v1), last revised 16 Aug 2026 (this v · 2022-09-14 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We establish a three-dimensional analogue of the classical theorem that a Latin square of order \(m\) can be embedded in a Latin square of order \(n\) if and only if \(n \ge 2m\). Let \(L\) be an \(n\times n\times n\) array. A {\it layer} of \(L\) is obtained by fixing one coordinate. If \(L\) is filled with \(n^2\) symbols so that every layer contains each symbol exactly once, then \(L\) is called a {\it layer-rainbow cube}. If \(L\) is filled with \(n\) symbols and every layer is a Latin square, then \(L\) is called a {\it layer-Latin cube}. Relatively little is known about embedding partial layer-Latin cubes, and the existing results are far from optimal with respect to the order of the containing cube. In contrast, no embedding results appear to be known for layer-rainbow cubes. We resolve this problem completely by proving that a layer-rainbow cube of order \(m\) can be embedded in a layer-rainbow cube of order \(n\) if and only if \(n \ge 2m\). Equivalently, our result may be viewed as an embedding theorem for one-factorizations of complete tripartite \(3\)-uniform hypergraphs.

Submission history

From: Amin Bahmanian [view email]
[v1] Wed, 14 Sep 2022 04:13:59 UTC (8 KB)
[v2] Sun, 16 Aug 2026 05:44:35 UTC (401 KB)