TY - JOUR
T1 - Global Convergence and Implementation of NGTN Method for Solving Large-Scale Sparse Nonlinear Programming Problems
AU - Qin Ni
JO - Journal of Computational Mathematics
VL - 4
SP - 337
EP - 346
PY - 2001
DA - 2001/08
SN - 19
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jcm/8986.html
KW - Nonlinear programming, Large-scale problem, Sparse.
AB - <p style="text-align: justify;">An NGTN method was proposed for solving large-scale sparse nonlinear programming (NLP) problems. This is a hybrid method of a truncated Newton direction and a modified negative gradient direction, which is suitable for handling sparse data structure and possesses Q-quadratic convergence rate. The global convergence of this new method is proved, the convergence rate is further analysed, and the detailed implementation is discussed in this paper. Some numerical tests for solving truss optimization and large sparse problems are reported. The theoretical and numerical results show that the new method is efficient for solving large-scale sparse NLP problems.</p>