A Class of Three-Dimensional Subspace Conjugate Gradient Algorithms for Unconstrained Optimization
Keyword(s):
In this paper, a three-parameter subspace conjugate gradient method is proposed for solving large-scale unconstrained optimization problems. By minimizing the quadratic approximate model of the objective function on a new special three-dimensional subspace, the embedded parameters are determined and the corresponding algorithm is obtained. The global convergence result of a given method for general nonlinear functions is established under mild assumptions. In numerical experiments, the proposed algorithm is compared with SMCG_NLS and SMCG_Conic, which shows that the given algorithm is robust and efficient.
2006 ◽
Vol 179
(2)
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pp. 407-430
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2009 ◽
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2016 ◽
Vol 32
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pp. 534-551
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2018 ◽
Vol 7
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pp. 92
2018 ◽
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