Computing Invariant Measures of Piecewise Convex Maps with Countably Many Branches

In this paper, we develop convergent numerical schemes to approximate absolutely continuous invariant measures of piecewise convex maps with countably many branches, based on a modified piecewise-linear Markov method and a novel piecewise quadratic B-spline method. The proposed schemes are effective for studying piecewise convex chaotic maps. Notably, while it ensures the same convergence order as the piecewise-linear method, the piecewise quadratic B-spline method enhances the smoothness of the approximate density. Numerical examples demonstrate the efficacy of these approaches.

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Publication Details

Journal
International Journal of Bifurcation and Chaos
Published
2026-07-16
DOI
https://doi.org/10.1142/s0218127426502032
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Computing Invariant Measures of Piecewise Convex Maps with Countably Many Branches

Jiu Ding, Congming Jin, Boyu Yang
International Journal of Bifurcation and Chaos
Markov Chains and Monte Carlo Methods
article

Computing Invariant Measures of Piecewise Convex Maps with Countably Many Branches

Jiu Ding, Congming Jin, Boyu Yang
article en

Abstract

In this paper, we develop convergent numerical schemes to approximate absolutely continuous invariant measures of piecewise convex maps with countably many branches, based on a modified piecewise-linear Markov method and a novel piecewise quadratic B-spline method. The proposed schemes are effective for studying piecewise convex chaotic maps. Notably, while it ensures the same convergence order as the piecewise-linear method, the piecewise quadratic B-spline method enhances the smoothness of the approximate density. Numerical examples demonstrate the efficacy of these approaches.

International Journal of Bifurcation and Chaos
Zhejiang Sci-Tech University (CN), Nanfang Hospital (CN)
Openalex Percentile: Top 7%
Markov Chains and Monte Carlo Methods
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