A quasi-newton quadratic penalty method for minimization subject to nonlinear equality constraints

被引：6

作者：

Coleman, TF ^{[1
]}

Liu, JG

Yuan, W

机构：

[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14850 USA

[2] Cornell Univ, Cornell Theory Ctr, Ithaca, NY 14850 USA

[3] Univ N Texas, Dept Math, Denton, TX 76203 USA

[4] Cornell Univ, Ctr Appl Math, Ithaca, NY 14853 USA

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2000年 / 15卷 / 02期

基金：

美国国家科学基金会;

关键词：

nonlinearly constrained optimization; equality constraints; quasi-Newton methods; BFGS; quadratic penalty function; reduced Hessian approximation;

D O I：

10.1023/A:1008730909894

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We present a modified quadratic penalty function method for equality constrained optimization problems. The pivotal feature of our algorithm is that at every iterate we invoke a special change of variables to improve the ability of the algorithm to follow the constraint level sets. This change of variables gives rise to a suitable block diagonal approximation to the Hessian which is then used to construct a quasi-Newton method. We show that the complete algorithm is globally convergent. Preliminary computational results are reported.

引用

页码：103 / 123

页数：21

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