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

WordPress大学
WordPress大学
V
V2EX
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
小众软件
小众软件
量子位
博客园 - 三生石上(FineUI控件)
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - Franky
博客园_首页
IT之家
IT之家
S
SegmentFault 最新的问题
博客园 - 叶小钗
阮一峰的网络日志
阮一峰的网络日志
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 聂微东
T
Tailwind CSS Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
爱范儿
爱范儿
月光博客
月光博客
有赞技术团队
有赞技术团队
H
Help Net Security
云风的 BLOG
云风的 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
Exact values of rainbow Turán numbers for fan graphs and ...
Xinmin Hou, Daoguang Xiong · 2026-05-31 · via math updates on arXiv.org

An edge-colored graph is called rainbow if all its edges have distinct colors. For a fixed graph $H$, the rainbow Turán number $\exstar(n, H)$ is the maximum number of edges in a properly edge-colored graph with $n$ vertices that does not contain a rainbow subgraph isomorphic to $H$. A $t$-fan $\Ft$ ($t \geq 2$) is a graph formed by $t$ triangles sharing a common vertex. A wheel graph $\Wn$ is constructed by connecting a new vertex $x$ to all vertices of a cycle $\Cn$ with $n$ vertices. Keevash, Mubayi, Sudakov, Verstraëte~({\small Combin. Probab. Comput., 2007}) showed that \[ \exstar(n, F_t) \geq \floor{\frac{n^2}{4}} + (t-1)\floor{\frac{n}{2}} \] by constructing extremal graphs for $n\not\equiv 2\pmod 4$. In this paper, we propose such a method: for a graph $H$, the upper bound of $\exstar(n, H)$ can be analyzed using the rainbow Turán number of $H'$, where $H'$ is obtained by deleting one vertex from $H$. By applying this method, we determine the exact values of the rainbow Turán numbers for two families of graphs when the number of vertices $n$ is sufficiently large. Specifically, we obtain: (1) when $n > 230t$, \[ \exstar(n, \Ft) = \floor{\frac{n^2}{4}} + (t-1)\floor{\frac{n}{2}} - \Dn, \] where $\Dn = 1$ if $n \equiv 2 \pmod{4}$, and $\Dn = 0$ otherwise; (2) for graphs $H$ satisfying $W_{2t} \subset H \subset \Kts$ ($t \leq s$), when $n$ is sufficiently large, \[ \exstar(n, H) = \begin{cases} \floor{\frac{n^2}{4}} + \floor{\frac{(t-1)n}{2}} - \Dn & \text{if } t \text{ is odd}, \\ \floor{\frac{n^2}{4}} + \floor{\frac{(t-1)n}{2}} & \text{if } t \text{ is even}. \end{cases} \]