An improved multi-step gradient-type method for large scale optimization

In this paper, we propose an improved multi-step diagonal updating method for large scale unconstrained optimization. Our approach is based on constructing a new gradient-type method by means of interpolating curves. We measure the distances required to parameterize the interpolating polynomials via...

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Main Authors: Farid, Mahboubeh, Leong, Wah June
Format: Article
Language:English
Published: Elsevier 2011
Online Access:http://psasir.upm.edu.my/id/eprint/24643/
http://psasir.upm.edu.my/id/eprint/24643/1/An%20improved%20multi.pdf
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author Farid, Mahboubeh
Leong, Wah June
author_facet Farid, Mahboubeh
Leong, Wah June
author_sort Farid, Mahboubeh
building UPM Institutional Repository
collection Online Access
description In this paper, we propose an improved multi-step diagonal updating method for large scale unconstrained optimization. Our approach is based on constructing a new gradient-type method by means of interpolating curves. We measure the distances required to parameterize the interpolating polynomials via a norm defined by a positive-definite matrix. By developing on implicit updating approach we can obtain an improved version of Hessian approximation in diagonal matrix form, while avoiding the computational expenses of actually calculating the improved version of the approximation matrix. The effectiveness of our proposed method is evaluated by means of computational comparison with the BB method and its variants. We show that our method is globally convergent and only requires O(n) memory allocations.
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spelling upm-246432019-10-11T00:43:03Z http://psasir.upm.edu.my/id/eprint/24643/ An improved multi-step gradient-type method for large scale optimization Farid, Mahboubeh Leong, Wah June In this paper, we propose an improved multi-step diagonal updating method for large scale unconstrained optimization. Our approach is based on constructing a new gradient-type method by means of interpolating curves. We measure the distances required to parameterize the interpolating polynomials via a norm defined by a positive-definite matrix. By developing on implicit updating approach we can obtain an improved version of Hessian approximation in diagonal matrix form, while avoiding the computational expenses of actually calculating the improved version of the approximation matrix. The effectiveness of our proposed method is evaluated by means of computational comparison with the BB method and its variants. We show that our method is globally convergent and only requires O(n) memory allocations. Elsevier 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/24643/1/An%20improved%20multi.pdf Farid, Mahboubeh and Leong, Wah June (2011) An improved multi-step gradient-type method for large scale optimization. Computers and Mathematics with Applications, 61 (11). pp. 3312-3318. ISSN 0898-1221; ESSN: 1873-7668 https://www.sciencedirect.com/science/article/pii/S0898122111003312?via%3Dihub 10.1016/j.camwa.2011.04.030
spellingShingle Farid, Mahboubeh
Leong, Wah June
An improved multi-step gradient-type method for large scale optimization
title An improved multi-step gradient-type method for large scale optimization
title_full An improved multi-step gradient-type method for large scale optimization
title_fullStr An improved multi-step gradient-type method for large scale optimization
title_full_unstemmed An improved multi-step gradient-type method for large scale optimization
title_short An improved multi-step gradient-type method for large scale optimization
title_sort improved multi-step gradient-type method for large scale optimization
url http://psasir.upm.edu.my/id/eprint/24643/
http://psasir.upm.edu.my/id/eprint/24643/
http://psasir.upm.edu.my/id/eprint/24643/
http://psasir.upm.edu.my/id/eprint/24643/1/An%20improved%20multi.pdf