A quasi-Newton algorithm for large-scale nonlinear equations

被引:4
|
作者
Huang, Linghua [1 ]
机构
[1] Guangxi Univ Finance & Econ, Sch Informat & Stat, Nanning 530003, Guangxi, Peoples R China
关键词
nonlinear equations; large-scale; conjugate gradient; quasi-Newton method; global convergence; CONJUGATE-GRADIENT ALGORITHM; NONMONOTONE LINE SEARCH; TRUST REGION ALGORITHM; BFGS METHOD; CONVERGENCE ANALYSIS; GLOBAL CONVERGENCE; DESCENT PROPERTY; OPTIMIZATION; BARZILAI;
D O I
10.1186/s13660-017-1301-7
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, the algorithm for large-scale nonlinear equations is designed by the following steps: (i) a conjugate gradient (CG) algorithm is designed as a sub-algorithm to obtain the initial points of the main algorithm, where the sub-algorithm's initial point does not have any restrictions; (ii) a quasi-Newton algorithm with the initial points given by sub-algorithm is defined as main algorithm, where a new nonmonotone line search technique is presented to get the step length ak. The given nonmonotone line search technique can avoid computing the Jacobian matrix. The global convergence and the 1 + q-order convergent rate of the main algorithm are established under suitable conditions. Numerical results show that the proposed method is competitive with a similar method for large-scale problems.
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页数:16
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