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

Engineering at Meta
Engineering at Meta
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
腾讯CDC
宝玉的分享
宝玉的分享
量子位
Recent Announcements
Recent Announcements
Martin Fowler
Martin Fowler
J
Java Code Geeks
V
Visual Studio Blog
阮一峰的网络日志
阮一峰的网络日志
Blog — PlanetScale
Blog — PlanetScale
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
S
SegmentFault 最新的问题
B
Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 【当耐特】
小众软件
小众软件
The Cloudflare Blog
Y
Y Combinator Blog
I
InfoQ
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
GbyAI
GbyAI
IT之家
IT之家

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
Proof of the Density Threshold Conjecture for Pinwheel Sc...
[Submitted on 25 Jun 2026] · 2026-06-26 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:In the pinwheel scheduling problem, each task $i$ is associated with a positive integer $a_i$ called its period, and we want to (perpetually) schedule one task per day so that each task $i$ is performed at least once every $a_i$ days. An obvious necessary condition for schedulability is that the density, defined as the sum of execution rates $1/a_i$, does not exceed $1$. We prove that all instances with density not exceeding $5/6$ are schedulable, as was conjectured by Chan and Chin in 1993. Like some of the known partial progress towards the conjecture, our proof involves computer search for schedules for a large but finite set of instances. A key idea in our reduction to these finite cases is to generalize the problem to fractional (non-integer) periods in an appropriate way. As byproducts of our ideas, we obtain a simple proof that every instance with two distinct periods and density at most $1$ is schedulable, as well as a fast algorithm for the bamboo garden trimming problem with approximation ratio $4/3$.

Submission history

From: Akitoshi Kawamura [view email]
[v1] Thu, 25 Jun 2026 14:41:23 UTC (49 KB)