Wasserstein convergence rates in the invariance principle for sequential dynamical systems

Abstract In this paper, we consider the convergence rate with respect to the Wasserstein distance in the invariance principle for sequential dynamical systems. We utilize and modify the techniques previously employed for stationary sequences to address our non-stationary case. Under certain assumptions, we can apply our result to a class of dynamical systems, including sequential β n -transformations, piecewise uniformly expanding maps with additive noise in one-dimensional and multidimensional case, and so on.

Authors

Institutions

Publication Details

Journal
Nonlinearity
Published
2024-11-07
DOI
https://doi.org/10.1088/1361-6544/ad8c1d
Citations
2
Primary Topic
Mathematical Dynamics and Fractals
Type
article
Field-Weighted Citation Impact
1.78

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Wasserstein convergence rates in the invariance principle for sequential dynamical systems

Zhenxin Liu, Zhe Wang
2 citations
Nonlinearity
Mathematical Dynamics and Fractals
1.78
article

Wasserstein convergence rates in the invariance principle for sequential dynamical systems

Zhenxin Liu, Zhe Wang
article en
2 citations

Abstract

Abstract In this paper, we consider the convergence rate with respect to the Wasserstein distance in the invariance principle for sequential dynamical systems. We utilize and modify the techniques previously employed for stationary sequences to address our non-stationary case. Under certain assumptions, we can apply our result to a class of dynamical systems, including sequential β n -transformations, piecewise uniformly expanding maps with additive noise in one-dimensional and multidimensional case, and so on.

NonlinearityVol. 37(12)
Dalian University of Technology (CN)
Dalian High-Level Talent Innovation Program, National Natural Science Foundation of China, Liaoning Revitalization Talents Program, National Key Research and Development Program of China
Openalex Percentile: Top 15%
Mathematical Dynamics and Fractals
1.78
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.