A subgradient method with non-monotone line search

O Ferreira, G Grapiglia (), E Santos and J Souza
Additional contact information
O Ferreira: UFG - Universidade Federal de Goiás [Goiânia]
G Grapiglia: UCL - Université Catholique de Louvain = Catholic University of Louvain
E Santos: IFCE - Instituto Federal de Educação, Ciência e Tecnologia do Maranhão
J Souza: Federal University of Piauí, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Keywords: Subgradient method; Non-monotone line search; Convex function (search for similar items in EconPapers)
Date: 2023-03
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03880925
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Published in Computational Optimization and Applications, 2023, 84 (2), pp.397-420. ⟨10.1007/s10589-022-00438-z⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03880925

DOI: 10.1007/s10589-022-00438-z

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