A subgradient method with non-monotone line search

A subgradient method with non-monotone line search

In this paper we present a subgradient method with non-monotone line search for the minimization of convex functions with simple convex constraints. Different from the standard subgradient method with prefixed step sizes, the new method selects the step sizes in an adaptive way. Under mild condition...

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Veröffentlicht in:Computational optimization and applications 2023-03, Vol.84 (2), p.397-420
Hauptverfasser: Ferreira, O. P., Grapiglia, G. N., Santos, E. M., Souza, J. C. O.
Format: Artikel
Sprache:eng
Schlagworte:
Convex analysis
Convex and Discrete Geometry
Economics and Finance
Humanities and Social Sciences
Iterative methods
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization
Statistics
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Zusammenfassung:In this paper we present a subgradient method with non-monotone line search for the minimization of convex functions with simple convex constraints. Different from the standard subgradient method with prefixed step sizes, the new method selects the step sizes in an adaptive way. Under mild conditions asymptotic convergence results and iteration-complexity bounds are obtained. Preliminary numerical results illustrate the relative efficiency of the proposed method.
ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-022-00438-z