Abstract
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.
Original language | English |
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Pages (from-to) | 772-794 |
Number of pages | 23 |
Journal | SIAM Journal on Optimization |
Volume | 22 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2012 |