Global Convergence and Implementation of NGTN Method for Solving Large-Scale Sparse Nonlinear Programming Problems
Year: 2001
Journal of Computational Mathematics, Vol. 19 (2001), Iss. 4 : pp. 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.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2001-JCM-8986
Journal of Computational Mathematics, Vol. 19 (2001), Iss. 4 : pp. 337–346
Published online: 2001-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 10
Keywords: Nonlinear programming Large-scale problem Sparse.