Limited memory quasi-newton method for large-scale linearly equality-constrained minimization

被引：1

作者：

Ni Qin

机构：

[1] Nanjing University of Aeronautics and Astronautics,LSEC, Institute of Computational Mathematics, the Academy of Mathematics and Systems Sciences CAS

[2] the Chinese Academy of Sciences,undefined

来源：

Acta Mathematicae Applicatae Sinica | 2000年 / 16卷 / 3期

关键词：

Limeted memory; quasi-Newton method; large-scale problem; linearly equality-constrained optimization;

D O I：

10.1007/BF02679897

中图分类号：

学科分类号：

摘要：

In this paper, a new limited memory quasi-Newton method is proposed and developed for solving large-scale linearly equality-constrained nonlinear programming problems. In every iteration, a linear equation subproblem is solved by using the scaled conjugate gradient method. A truncated solution of the subproblem is determined so that computation is decreased. The technique of limited memory is used to update the approximated inverse Hessian matrix of the Lagrangian function. Hence, the new method is able to handle large dense problems. The convergence of the method is analyzed and numerical results are reported.

引用

页码：320 / 328

页数：8

共 15 条

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