A Fast Multiple Change-Point Detection Method via Generalized Nearly Isotonic Optimization.

Luoxin Wang, Mengmeng Wang, Baisuo Jin, Yuehua Wu

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Luoxin WangDepartment of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China.ORCID 0000-0003-3244-6317

Mengmeng WangCollege of Mathematics and System Science, Xinjiang University, Urumqi 830017, China.

Baisuo JinSchool of Mathematical Sciences, Xinjiang Normal University, Urumqi 830017, China.

Yuehua WuDepartment of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.ORCID 0000-0002-8858-1289

Funding

The Natural Science and Engineering Research Council of Canada RGPIN-2023-05655University of Science and Technology of China-Xinjiang 288 Normal University Counterpart Cooperation and Development Joint Fund KY2040002501

6 · The paper itself

Abstract

In this paper, we study the generalized nearly isotonic optimization (GNIO) model and its dynamic programming solution (GNIO-DP). We introduce randomness into the GNIO-DP algorithm, enabling its first application to change-point detection and resulting in an O(n) complexity multiple change-point detection method. At the same time, we provide the theoretical properties of the change-point detection and prove the reliability and effectiveness of the GNIO-DP algorithm for this task. The simulation results show that our method has strong change-point detection ability. Compared with traditional methods, our method is faster in most scenarios.

Indexed as

dynamic programminggeneralized nearly isotonic optimizationmultiple change-point detectionO(n) complexity

Identifiers

PMID42793873

DOI10.3390/e28090961

PMCPMC13605911

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.