A new nonmonotone spectral projected gradient algorithm for box-constrained optimization problems in m×n real matrix space with application in image clustering

Box-constrained optimization problems in the real m×n matrix space have been widely applied in big data mining. However, efficient solution of them is still a challenge. In this paper, a new nonmonotone line search rule is first proposed by extending the well-known ones and inheriting their advantag...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of computational and applied mathematics Ročník 438; s. 115563
Hlavní autori: Li, Ting, Wan, Zhong, Guo, Jie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.03.2024
Predmet:
Algorithms
Convergence
Line search
Optimization
Line search
90C30
65K05
Algorithms
Optimization
Convergence
ISSN:0377-0427, 1879-1778
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Box-constrained optimization problems in the real m×n matrix space have been widely applied in big data mining. However, efficient solution of them is still a challenge. In this paper, a new nonmonotone line search rule is first proposed by extending the well-known ones and inheriting their advantages. Then, by analyzing and exploiting properties of this rule, a new nonmonotone spectral projected gradient algorithm is developed to solve the box-constrained optimization problems in the matrix space. Global convergence of the developed algorithm is also established. Numerical tests are conducted on a series of randomly generated test problems and those in the set of benchmark test problems. Compared with other existing nonmonotone line search rules, our rule shows its advantages in terms of the significantly reduced number of function evaluations and significantly reduced number of iterations. To further validate applicability of this research, we apply the studied optimization problem and the developed algorithm to solve the problems of image clustering. Numerical results demonstrate that the proposed method can generate better clustering results and is more robust than the similar ones available in the literature.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2023.115563