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

量子位
D
DataBreaches.Net
Microsoft Security Blog
Microsoft Security Blog
V
Visual Studio Blog
GbyAI
GbyAI
美团技术团队
云风的 BLOG
云风的 BLOG
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
博客园 - 叶小钗
Engineering at Meta
Engineering at Meta
博客园 - 三生石上(FineUI控件)
N
Netflix TechBlog - Medium
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
G
Google Developers Blog
博客园 - 【当耐特】
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
宝玉的分享
宝玉的分享
阮一峰的网络日志
阮一峰的网络日志
T
The Blog of Author Tim Ferriss
Y
Y Combinator Blog
U
Unit 42
P
Proofpoint News Feed
V
V2EX

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
Maximal Averages on the Affine Group $G_n$ and applications
[Submitted on 4 Feb 2026 (v1), last revised 4 Aug 2026 (this ver · 2026-02-05 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:Let \(G_n=\mathbb R^n\rtimes\mathbb R_+\) be equipped with the left Haar measure \(
d\mu(x,y)=\frac{dx\,dy}{y^{n+1}}. \) We study maximal averages associated with three basic motions on \(G_n\): horizontal translations, vertical dilations, and fixed hyperbolic geodesics in the upper half-space model. The translation maximal operator is the Euclidean Hardy--Littlewood maximal operator on each horizontal slice. The Haar-compatible dilation maximal operator is of weak type \((1,1)\) and bounded on \(L^p(G_n)\) for \(1<p\le\infty\), but it is not strongly bounded on \(L^1(G_n)\). By contrast, the unweighted Lebesgue dilation average is unbounded on every finite \(L^p(G_n)\) and is not of weak type \((1,1)\).
For fixed hyperbolic geodesic averages, the large-time part is strongly bounded on \(L^1(G_n)\) because of modular exponential decay. The small-time part is a finite-type parabolic maximal problem. Using the corresponding local finite-type \(L\log\log L\) endpoint estimate for the geodesic slice, we prove \(
\mathcal M_{\gamma_\omega}:L\log\log L(G_n)
\longrightarrow L^{1,\infty}(G_n) \) in weak Orlicz form, together with the strong \(L^p(G_n)\) bounds for \(1<p\le\infty\). We also show that the strong \(L^1\) endpoint fails. Finally, we record a discrete random-walk maximal inequality whose sufficient condition is expressed through the modular drift $$
\rho_p(\sigma)=\int_{G_n}y(h)^{n/p}\,d\sigma(h), $$ where \(\sigma\) is the probability measure defining the right random walk.

Submission history

From: Chaojie Wen [view email]
[v1] Wed, 4 Feb 2026 23:19:16 UTC (22 KB)
[v2] Tue, 4 Aug 2026 04:50:20 UTC (21 KB)