A New Adaptive Subspace Minimization Three-Term Conjugate Gradient Algorithm for Unconstrained Optimization

A New Adaptive Subspace Minimization Three-Term Conjugate Gradient Algorithm for Unconstrained Optimization

Year:    2021

Author:    Keke Zhang, Hongwei Liu, Zexian Liu

Journal of Computational Mathematics, Vol. 39 (2021), Iss. 2 : pp. 159–177

Abstract

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this paper. The search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces, and we also proposed an adaptive rule for choosing different searching directions at each iteration. We obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent condition. With the used nonmonotone line search, we prove that the new algorithm is globally convergent for general nonlinear functions under some mild assumptions. Numerical experiments show that the proposed algorithm is promising for the given test problem set.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1907-m2018-0173

Journal of Computational Mathematics, Vol. 39 (2021), Iss. 2 : pp. 159–177

Published online:    2021-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    19

Keywords:    Conjugate gradient method Nonmonotone line search Subspace minimization Sufficient descent condition Global convergence.

Author Details

Keke Zhang

Hongwei Liu

Zexian Liu

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