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

Blog — PlanetScale
Blog — PlanetScale
N
Netflix TechBlog - Medium
博客园 - 司徒正美
The GitHub Blog
The GitHub Blog
G
Google Developers Blog
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
Google DeepMind News
Google DeepMind News
博客园 - 【当耐特】
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Recent Announcements
Recent Announcements
aimingoo的专栏
aimingoo的专栏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Y
Y Combinator Blog
B
Blog RSS Feed
人人都是产品经理
人人都是产品经理
MongoDB | Blog
MongoDB | Blog
量子位
博客园 - Franky
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
The Cloudflare Blog
有赞技术团队
有赞技术团队
Jina AI
Jina AI
GbyAI
GbyAI

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
Cesàro means of firmly nonexpansive iterates need not con...
[Submitted on 25 May 2026 (v1), last revised 2 Jul 2026 (this ve · 2026-05-26 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:Firmly nonexpansive operators arise naturally as resolvents of monotone operators and as generalizations of projections and proximal mappings in convex optimization and fixed point theory. While their iterates are known to converge weakly to a fixed point, strong convergence is not guaranteed (Genel and Lindenstrauss, 1975). Strong convergence of Cesàro means of iterates is also known to fail for general nonlinear nonexpansive mappings (Krengel and Lin, 1987).
In this paper, we show that this failure persists in the much smaller class of firmly nonexpansive mappings. Using suitable meshes, we construct a new explicit family of counterexamples in infinite-dimensional Hilbert spaces with the origin as the unique fixed point. In the harmonic case, the Cesàro means of the iterates remain bounded away from the origin. Another variant yields Cesàro means that converge strongly to the origin. A third variant presents Cesàro means whose norms oscillate in the sense that their liminf is zero while their limsup is positive. Thus the strong convergence conclusion in von Neumann's linear mean ergodic theorem does not extend to Baillon's nonlinear mean ergodic theorem, even for firmly nonexpansive mappings.

Submission history

From: Tran Thanh Tung [view email]
[v1] Mon, 25 May 2026 06:51:26 UTC (20 KB)
[v2] Thu, 2 Jul 2026 02:38:27 UTC (22 KB)