Journal of Applied Mathematics

Accumulative Approach in Multistep Diagonal Gradient-Type Method for Large-Scale Unconstrained Optimization

Mahboubeh Farid, Wah June Leong, and Lihong Zheng

Full-text: Open access

Abstract

This paper focuses on developing diagonal gradient-type methods that employ accumulative approach in multistep diagonal updating to determine a better Hessian approximation in each step. The interpolating curve is used to derive a generalization of the weak secant equation, which will carry the information of the local Hessian. The new parameterization of the interpolating curve in variable space is obtained by utilizing accumulative approach via a norm weighting defined by two positive definite weighting matrices. We also note that the storage needed for all computation of the proposed method is just O ( n ) . Numerical results show that the proposed algorithm is efficient and superior by comparison with some other gradient-type methods.

Article information

Source
J. Appl. Math., Volume 2012 (2012), Article ID 875494, 11 pages.

Dates
First available in Project Euclid: 14 December 2012

Permanent link to this document
https://projecteuclid.org/euclid.jam/1355495201

Digital Object Identifier
doi:10.1155/2012/875494

Mathematical Reviews number (MathSciNet)
MR2948132

Zentralblatt MATH identifier
1254.90226

Citation

Farid, Mahboubeh; June Leong, Wah; Zheng, Lihong. Accumulative Approach in Multistep Diagonal Gradient-Type Method for Large-Scale Unconstrained Optimization. J. Appl. Math. 2012 (2012), Article ID 875494, 11 pages. doi:10.1155/2012/875494. https://projecteuclid.org/euclid.jam/1355495201


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