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

S
SegmentFault 最新的问题
博客园 - 三生石上(FineUI控件)
WordPress大学
WordPress大学
博客园 - 【当耐特】
月光博客
月光博客
Vercel News
Vercel News
D
Docker
I
InfoQ
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 叶小钗
MongoDB | Blog
MongoDB | Blog
GbyAI
GbyAI
有赞技术团队
有赞技术团队
雷峰网
雷峰网
博客园 - 聂微东
小众软件
小众软件
Y
Y Combinator Blog
腾讯CDC
L
LangChain Blog
The GitHub Blog
The GitHub Blog
宝玉的分享
宝玉的分享
Stack Overflow Blog
Stack Overflow Blog
大猫的无限游戏
大猫的无限游戏
T
The Blog of Author Tim Ferriss

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
Chamber lifting and non-radial Dunkl multipliers
[Submitted on 31 May 2026] · 2026-06-02 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We study non-radial Dunkl multipliers via chamber lifting. For an arbitrary finite reflection group $G$, the chamber lifting records all reflected values of a function and conjugates a multiplier into a finite matrix-valued operator on the chamber. If the dyadic matrix entries admit off-diagonal kernels satisfying the chamber $L^2$ Hörmander condition $\operatorname{CH}^2_{s,\eta}$ with $s>N_\kappa/2$, then the original multiplier is bounded on $L^p(\mathbb R^N,d\omega)$ for every $1<p<\infty$.
For the product reflection group $\Sigma_N=A_1^N\simeq\mathbb Z_2^N$ this chamber condition follows from scalar Sobolev conditions on the Walsh pieces of the multiplier. The tensor product of the one-dimensional even/odd Dunkl decompositions, together with the finite Walsh transform, identifies each lifted matrix entry with a Hankel multiplier acting between parity components. Wall separation and a scale-invariant $L^2$ Sobolev condition of order $\sigma>N_\kappa/2$ therefore imply $L^p$ boundedness, for all $1<p<\infty$, for a genuinely non-radial class of symbols. The order $N_\kappa/2$ is forced already by the rank-one Bessel transform.
The same chamber theorem also applies to non-product examples once the matrix kernel condition is known, including the dihedral groups $I_2(q)$ and hence $A_2\simeq I_2(3)$ and $B_2\simeq I_2(4)$. The scalar Walsh--Sobolev verification is specific to $A_1^N$. In non-product groups such as $A_2$, $A_{N-1}$, and $B_N$, the product parity calculus is absent, so a scalar theorem of the same form would require additional transform estimates.

Submission history

From: Liangchuan Wu [view email]
[v1] Sun, 31 May 2026 10:02:44 UTC (34 KB)