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Journal of Applied Mathematics and Computing
Published in: Journal of Applied Mathematics and Computing 1-2/2014

01-10-2014 | Original Research

A nonmonotone supermemory gradient algorithm for unconstrained optimization

Authors: Yigui Ou, Yuanwen Liu

Published in: Journal of Applied Mathematics and Computing | Issue 1-2/2014

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Abstract

This paper presents a nonmonotone supermemory gradient algorithm for unconstrained optimization problems. At each iteration, this proposed method sufficiently uses the previous multi-step iterative information and avoids the storage and computation of matrices associated with the Hessian of objective functions, thus it is suitable to solve large-scale optimization problems and can converge stably. Under some assumptions, the convergence properties of the proposed algorithm are analyzed. Numerical results are also reported to show the efficiency of this proposed method.
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Metadata
Title
A nonmonotone supermemory gradient algorithm for unconstrained optimization
Authors
Yigui Ou
Yuanwen Liu
Publication date
01-10-2014
Publisher
Springer Berlin Heidelberg
Published in
Journal of Applied Mathematics and Computing / Issue 1-2/2014
Print ISSN: 1598-5865
Electronic ISSN: 1865-2085
DOI
https://doi.org/10.1007/s12190-013-0747-0

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