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

V
Visual Studio Blog
罗磊的独立博客
宝玉的分享
宝玉的分享
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Tailwind CSS Blog
博客园_首页
量子位
月光博客
月光博客
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
博客园 - 司徒正美
人人都是产品经理
人人都是产品经理
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
爱范儿
爱范儿
S
SegmentFault 最新的问题
雷峰网
雷峰网
小众软件
小众软件
博客园 - 聂微东
美团技术团队
Apple Machine Learning Research
Apple Machine Learning Research
WordPress大学
WordPress大学
Jina AI
Jina AI
Hugging Face - Blog
Hugging Face - 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
The Fractional Haemers Bound of the Mycielski Construction
[Submitted on 13 Jul 2025 (v1), last revised 21 Jul 2026 (this v · 2025-07-14 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We investigate the effect of the generalized Mycielski construction $M_r(G)$ on the complementary fractional Haemers bound $\bar{\mathcal{H}}_f(G; \mathbb{F})$, a parameter that depends on a graph $G$ and a field $\mathbb{F}$. The effect of the Mycielski construction on graph parameters has already been studied for the fractional chromatic number $\chi_f$ and the complementary Lovász theta number $\bar{\vartheta}$. Larsen, Propp, and Ullman provided a formula for $ \chi_f(M_2(G)) $ in terms of $\chi_f(G)$. This was later generalized by Tardif to $ \chi_f(M_r(G)) $ for any $r$, and Simonyi and the author gave a similar expression for $ \bar{\vartheta}(M_2(G)) $ in terms of $\bar{\vartheta}(G)$. In this paper, we show that Tardif's formula for the fractional chromatic number remains valid for $ \bar{\mathcal{H}}_f $ whenever $ \bar{\mathcal{H}}_f(G; \mathbb{F})$ equals the clique number of $G$. In particular, we provide a general upper bound on $\bar{\mathcal{H}}_f(M_r(G); \mathbb{F})$ in terms of $\bar{\mathcal{H}}_f(G;\mathbb{F})$ and we prove that this bound is tight whenever $ \bar{\mathcal{H}}_f(G; \mathbb{F})$ equals the clique number of $G$. Using the bounds, we present a general class of graphs for which the fractional Haemers bound of the generalized Mycielski construction can be determined exactly.

Submission history

From: Bence Csonka [view email]
[v1] Sun, 13 Jul 2025 22:02:27 UTC (16 KB)
[v2] Tue, 21 Jul 2026 09:16:24 UTC (16 KB)