A Limited-Memory BFGS Algorithm Based on a Trust-Region Quadratic Model for Large-Scale Nonlinear Equations.

A Limited-Memory BFGS Algorithm Based on a Trust-Region Quadratic Model for Large-Scale Nonlinear Equations.
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基于信任域二次模型的大规模非线性方程的有限内存BFGS算法

DOI:
10.1371/journal.pone.0120993
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发表时间:
2015
期刊:
影响因子:
3.7
通讯作者:
Wei Z
Wei Z
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Li Y;Yuan G;Wei Z

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提出了一种求解大规模非线性方程组的信赖域算法,在信赖域子问题中引入有限记忆BFGS(L-M-BFGS)更新矩阵,以提高算法求解大规模问题的有效性.在适当的条件下证明了该方法的全局收敛性。算例结果表明,该方法与范数方法相比具有较好的收敛性。
In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the limited-memory BFGS (L-M-BFGS) update matrix is used in the trust-region subproblem to improve the effectiveness of the algorithm for large-scale problems. The global convergence of the presented method is established under suitable conditions. The numerical results of the test problems show that the method is competitive with the norm method.
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