期刊
SIAM JOURNAL ON OPTIMIZATION
卷 22, 期 3, 页码 772-794出版社
SIAM PUBLICATIONS
DOI: 10.1137/110844362
关键词
nonlinear programming; linear constraint programming; Robinson's method; spectral gradient method; filter
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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