A SEQUENTIAL LINEAR CONSTRAINT PROGRAMMING ALGORITHM FOR NLP

被引：7
作者：
Fletcher, Roger [1 ]
机构：
[1] Univ Dundee, Div Math, Dundee DD1 4HN, Scotland
来源：
SIAM JOURNAL ON OPTIMIZATION | 2012年 / 22卷 / 03期
关键词：
nonlinear programming; linear constraint programming; Robinson's method; spectral gradient method; filter; GLOBAL CONVERGENCE; OPTIMIZATION;
D O I：
10.1137/110844362
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A new method for nonlinear programming (NLP) using sequential linear constraint programming (SLCP) is described. Linear constraint programming (LCP) subproblems are solved by a new code using a recently developed spectral gradient method for minimization. The method requires only first derivatives and avoids having to store and update approximate Hessian or reduced Hessian matrices. Globalization is provided by a trust region filter scheme. Open source production quality software is available. Results on a large selection of CUTEr test problems are presented and discussed and show that the method is reliable and reasonably efficient.
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收藏
页码：772 / 794
页数：23
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