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Abstract
In this paper, we provide detailed discussions on the probability estimates of quadrature rules from uniformly sampled points on spheres. Besides gathering relevant lemmas in the literature to derive probability estimates on the existence of exact quadrature rules for spherical harmonics, we provide additional estimates with finer characterizations based on probabilistic quantities related to the measure and the diameter of Voronoi cells. Specifically, our estimates provide additional affirmative answers to certain relations between the number of sampled points and the degree of spherical harmonics. We further investigate the problem of setting the number of points to be of order td. Simple analysis based on our estimates suggests that the constant in the order cannot be fixed for all t and should increase as t increases. This is empirically verified in our experiments. © 2026 World Scientific Publishing Company.
| Original language | English |
|---|---|
| Journal | Analysis and Applications |
| Online published | 19 Nov 2025 |
| DOIs | |
| Publication status | Online published - 19 Nov 2025 |
Funding
This paper was supported in part by the Research Grants Council of Hong Kong (Project Nos. CityU 11309122, CityU 11302023, CityU 11301224, and CityU 11300825) and the National Natural Science Foundation of China (NSFC 12471400).
Research Keywords
- d-dimensional sphere
- exact quadrature rule
- nested structure
- probability estimate
- spherical designs
- uniform distribution
- Voronoi cell
Publisher's Copyright Statement
- COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: Electronic version of an article published as Analysis and Applications. Advance online publication. 10.1142/S0219530526500119 © 2026 World Scientific Publishing Company. https://www.worldscientific.com/worldscinet/aa
RGC Funding Information
- RGC-funded
Fingerprint
Dive into the research topics of 'On the probability estimates of quadrature rules from uniformly sampled points on spheres'. Together they form a unique fingerprint.Projects
- 4 Active
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GRF: Nested Spherical Designs: Theory, Computation, and Applications
ZHUANG, X. (Principal Investigator / Project Coordinator)
1/01/26 → …
Project: Research
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GRF: Graph Framelets for Graph Deep Learning: Homophily and Heterophily
ZHUANG, X. (Principal Investigator / Project Coordinator) & LI, M. (Co-Investigator)
1/01/25 → …
Project: Research
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GRF: Framelets for Geometric Deep Learning: Spheres, Graphs, and Neural Networks
ZHUANG, X. (Principal Investigator / Project Coordinator) & WANG, Y. G. (Co-Investigator)
1/01/24 → …
Project: Research
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