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Journal of Applied Mathematics
Volume 2013 (2013), Article ID 854890, 6 pages
http://dx.doi.org/10.1155/2013/854890
Research Article

A Generalized Gradient Projection Filter Algorithm for Inequality Constrained Optimization

1Department of Mathematics, East China University of Science and Technology, Shanghai 200237, China
2China UnionPay Merchant Services Co., Ltd., Shanghai 200135, China

Received 27 May 2013; Revised 22 August 2013; Accepted 5 September 2013

Academic Editor: Igor Andrianov

Copyright © 2013 Wei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A generalized gradient projection filter algorithm for inequality constrained optimization is presented. It has three merits. The first is that the amount of computation is lower, since the gradient matrix only needs to be computed one time at each iterate. The second is that the paper uses the filter technique instead of any penalty function for constrained programming. The third is that the algorithm is of global convergence and locally superlinear convergence under some mild conditions.