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Volume 19, Issue 4
Global Convergence and Implementation of NGTN Method for Solving Large-Scale Sparse Nonlinear Programming Problems

Qin Ni

J. Comp. Math., 19 (2001), pp. 337-346.

Published online: 2001-08

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  • Abstract

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.

  • Keywords

Nonlinear programming, Large-scale problem, Sparse.

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COPYRIGHT: © Global Science Press

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@Article{JCM-19-337, author = {Qin Ni}, title = {Global Convergence and Implementation of NGTN Method for Solving Large-Scale Sparse Nonlinear Programming Problems}, journal = {Journal of Computational Mathematics}, year = {2001}, volume = {19}, number = {4}, pages = {337--346}, abstract = {

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.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/8986.html} }
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 -

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.

Qin Ni. (2001). Global Convergence and Implementation of NGTN Method for Solving Large-Scale Sparse Nonlinear Programming Problems. Journal of Computational Mathematics. 19 (4). 337-346. doi:
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