A proximal-based algorithm for piecewise sparse approximation with application to scattered data fitting

• Autorzy: Zhong Yijun , Li Chongjun , Li Zhong , Duan Xiaojuan

Identyfikatory

DOI

10.34768/amcs-2022-0046

• Języki publikacji: EN

Abstrakty

EN

In some applications, there are signals with a piecewise structure to be recovered. In this paper, we propose a piecewise sparse approximation model and a piecewise proximal gradient method (JPGA) which aim to approximate piecewise signals. We also make an analysis of the JPGA based on differential equations, which provides another perspective on the convergence rate of the JPGA. In addition, we show that the problem of sparse representation of the fitting surface to the given scattered data can be considered as a piecewise sparse approximation. Numerical experimental results show that the JPGA can not only effectively fit the surface, but also protect the piecewise sparsity of the representation coefficient.

Słowa kluczowe

EN

piecewise sparse approximation; proximal gradient; scattered data fitting

PL

aproksymacja rzadka; gradient proksymalny; dopasowanie danych

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 4
• Strony: 671--682
• Opis fizyczny: Bibliogr. 27 poz., tab., wykr.

Twórcy

autor

Zhong Yijun

• Department of Mathematical Sciences, Zhejiang Sci-Tech University, No. 928, No. 2 Street, Xiasha Higher Education Park, Hangzhou, China

autor

Li Chongjun

• School of Mathematical Sciences, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, China

autor

Li Zhong

• Department of Science and Technology, Huzhou University, 759 Erhuan East Road, Huzhou, China

autor

Duan Xiaojuan

• Department of Mathematical Sciences, Zhejiang Sci-Tech University, No. 928, No. 2 Street, Xiasha Higher Education Park, Hangzhou, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)