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

雷峰网
雷峰网
宝玉的分享
宝玉的分享
量子位
博客园 - Franky
The Cloudflare Blog
博客园 - 【当耐特】
Jina AI
Jina AI
Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
Microsoft Security Blog
Microsoft Security Blog
博客园 - 叶小钗
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
罗磊的独立博客
V
V2EX
MongoDB | Blog
MongoDB | Blog
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
H
Help Net Security
博客园 - 聂微东
F
Fortinet All Blogs
GbyAI
GbyAI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
人人都是产品经理
人人都是产品经理

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
Efficient Multi-Precision Computation of Bessel Functions...
[Submitted on 14 May 2025 (v1), last revised 12 Jun 2026 (this v · 2026-06-15 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper is the first in a series devoted to the development of efficient and highly accurate algorithms, with multiprecision \texttt{Fortran} implementations, for the computation of Bessel functions. In this first part, we present a \emph{novel, self-contained, efficient,} and \emph{multiprecision} algorithm for evaluating the modified Bessel function of the first kind, $I_{\nu}(z)$. The method integrates several analytic representations of $I_{\nu}(z)$, carefully selected to ensure both high accuracy and suitability for high-precision computation, together with optimally determined transition boundaries between computational regions. This design achieves high efficiency while fully preserving numerical accuracy. Unlike other widely used algorithms and libraries, such as AMOS, Boost, and GSL, which either reject negative orders $\nu$ or rely on special-case symmetries valid only for integer orders, the present algorithm provides a stable approach for evaluating $I_{\nu}(z)$ for arbitrary real orders, including $\nu < 0$, and complex arguments $z$. The developed robust \texttt{Fortran} implementation provides support for both double and native quadruple-precision arithmetic. The availability of quadruple precision further enhances numerical stability, extends the reliable computational domain in $(\nu, |z|)$ by approximately an order of magnitude in each direction, and enables accuracies exceeding 26 significant digits. This advancement substantially broadens the applicability of the method to demanding high-precision problems in science and engineering. Compared to AMOS (Algorithm~644), which is restricted to double precision, the present algorithm exhibits superior accuracy and efficiency, with benchmark tests demonstrating execution times reduced to 38--71\% of those of AMOS in double precision.

Submission history

From: Mofreh Zaghloul [view email]
[v1] Wed, 14 May 2025 20:00:25 UTC (1,761 KB)
[v2] Fri, 12 Jun 2026 07:31:13 UTC (2,369 KB)