A Quasi-Newton Quadratic Penalty Method for Minimization Subject to Nonlinear Equality Constraints

被引：0
|
作者：
Thomas F. Coleman
Jianguo Liu
Wei Yuan
机构：
[1] Cornell University,Computer Science Department and Cornell Theory Center
[2] University of North Texas,Department of Mathematics
[3] Cornell University,Center for Applied Mathematics
来源：
Computational Optimization and Applications | 2000年 / 15卷
关键词：
nonlinearly constrained optimization; equality constraints; quasi-Newton methods; BFGS; quadratic penalty function; reduced Hessian approximation;
D O I：
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中图分类号：
学科分类号：
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.
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页码：103 / 123
页数：20
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