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February 11, 2026Mathematische Annalen2 citations

Weighted Birkhoff averages: Deterministic and probabilistic perspectives

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ZTZhicheng TongYLYong Li

Key Points

  • The research aims to establish rapid convergence results for weighted Birkhoff averages from both deterministic and probabilistic perspectives.
  • Surveyed applications of weighted quasi-Monte Carlo methods.
  • Introduced weighting with compact support for Birkhoff ergodic averages.
  • Explored the probabilistic perspective including strong law of large numbers and central limit theorem.
  • Achieved universal rapid convergence for various periodic systems.
  • Established exponential convergence results for weighted Fourier coefficient computations.
  • Discussed general weighting functions and their improvements over existing results.

Abstract

In this paper, we survey physically related applications of a class of weighted quasi-Monte Carlo methods from a theoretical, deterministic perspective, and establish quantitative universal rapid convergence results via various regularity assumptions. Specifically, we introduce weighting with compact support to the Birkhoff ergodic averages of quasi-periodic, almost periodic, and periodic systems, thereby achieving universal rapid convergence, including both arbitrary polynomial and exponential types. This is in stark contrast to the typically slow convergence in classical ergodic theory. As new contributions, we not only discuss more general weighting functions but also provide quantitative improvements to existing results; the explicit regularity settings facilitate the application of these methods to specific problems. We also revisit the physically related problems and, for the first time, establish universal exponential convergence results for the weighted computation of Fourier coefficients, in both finite-dimensional and infinite-dimensional cases. In addition to the above, we explore results from a probabilistic perspective, including the weighted strong law of large numbers and the weighted central limit theorem, by building upon the historical results.

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Cite This Study

Tong et al. (2026) studied this question.

synapsesocial.com/papers/698be001058ab1890a13ba74https://doi.org/10.1007/s00208-026-03311-0
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