@Article{JCM-39-2,
author = {Zhang, Keke and Liu, Hongwei and Zexian, Liu},
title = {A New Adaptive Subspace Minimization Three-Term Conjugate Gradient Algorithm for Unconstrained Optimization},
journal = {Journal of Computational Mathematics},
year = {2021},
volume = {39},
number = {2},
pages = {159--177},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1907-m2018-0173},
url = {https://global-sci.com/article/84223/a-new-adaptive-subspace-minimization-three-term-conjugate-gradient-algorithm-for-unconstrained-optimization}
}