Acta mathematica scientia,Series A ›› 2018, Vol. 38 ›› Issue (5): 954-962.

Previous Articles     Next Articles

A Class of Modified Non-Monotonic Spectral Conjugate Gradient Method and Applications to Non-Negative Matrix Factorization

Xiangli Li1,*(),Juanjuan Shi2,Xiaoliang Dong3   

  1. 1 School of Mathematics and Computing Science, Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guangxi Guilin 541004
    2 Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guangxi Guilin 541004
    3 School of Mathematics and Information Science, North Minzu University, Yinchuan 750021
  • Received:2016-10-24 Online:2018-11-09 Published:2018-11-09
  • Contact: Xiangli Li E-mail:lixiangli@guet.edu.cn
  • Supported by:
    the NSFC(71561008);the NSFC(11601012);the Guangxi Natural Science Foundation(2018GXNSFAA138169);the Guangxi Key Laboratory of Automatic Detection Technology and Instruments(YQ16112);the Guangxi Key Laboratory of Cryptography Security(GCIS201708);the Natural Science Foundation of Ningxia(NZ17103)

Abstract:

Spectral conjugate gradient algorithm is an effective method to solve unconstrained optimization problems. It is based on the conjugate gradient method and combines the spectral method to maintain the advantages of the two methods. In this paper, we propose a class of modified non-monotonic spectral conjugate gradient algorithm, under certain assumptions, the convergence of the algorithm is proved. In addition, we applied the algorithm to the nonnegative matrix factorization, and the numerical results show that the algorithm is effective.

Key words: Unconstrained optimization, Spectral conjugate gradient method, Non monotone line search, Non negative matrix factorization

CLC Number: 

  • O221
Trendmd