Subspace trust-region algorithm with conic model  for       unconstrained optimization

Xin Zhang; Jie Wen; Qin Ni

doi:10.3934/naco.2013.3.223

Article Contents

2013, Volume 3, Issue 2: 223-234. Doi: 10.3934/naco.2013.3.223

This issue Previous Article Partial eigenvalue assignment with time delay robustness Next Article A unified maximum entropy method via spline functions for Frobenius-Perron operators

Subspace trust-region algorithm with conic model for unconstrained optimization

1.
Jinling College of Nanjing University, Nanjing 210089
2.
Nanjing University of Aeronautics and Astronautics, Nanjing 210016

Received: December 2011

Revised: January 2013

Published: April 2013

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Abstract

In this paper, a new subspace algorithm is proposed for unconstrained optimization. In this new algorithm, the subspace technique is used in the trust region subproblem with conic model, and the dogleg method is modified to solve this subproblem. The global convergence of this algorithm under some reasonable conditions is established. Numerical experiment shows that this algorithm may be superior to the corresponding algorithm without using subspace technique especially for large scale problems.

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Mathematics Subject Classification: Primary: 90C30; Secondary: 65K05.

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References

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