Nonmonotonic back-tracking trust region interior point algorithm for linear constrained optimization

Authors:
Detong Zhu
Affiliations:
Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
Venue:
Journal of Computational and Applied Mathematics
Year:
2003

Citing 8
Cited 2

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Abstract

In this paper, we modify the trust region interior point algorithm proposed by Bonnans and Pola in (SIAM J. Optim. 7(3) (1997) 717) for linear constrained optimization. A mixed strategy using both trust region and line-search techniques is adopted which switches to back-tracking steps when a trial step produced by the trust region subproblem may be unacceptable. The global convergence and local convergence rate of the improved algorithm are established under some reasonable conditions. A nonmonotonic criterion is used to speed up the convergence progress in some ill-conditioned cases. The results of numerical experiments are reported to show the effectiveness of the proposed algorithm.