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

01.10.2014 | Original Research

A nonmonotone supermemory gradient algorithm for unconstrained optimization

verfasst von: Yigui Ou, Yuanwen Liu

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 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.
Vorheriger Artikel Recurrence analysis on Julia sets of semigroups of complex polynomials
Nächster Artikel Triple scale analysis of periodic solutions and resonance of some asymmetric non linear vibrating systems

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Metadaten
Titel
A nonmonotone supermemory gradient algorithm for unconstrained optimization
verfasst von
Yigui Ou
Yuanwen Liu
Publikationsdatum
01.10.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Applied Mathematics and Computing / Ausgabe 1-2/2014
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI
https://doi.org/10.1007/s12190-013-0747-0

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