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 |
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| container_title | Computational optimization and applications |
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| creator | Ferreira, O. P. Grapiglia, G. N. Santos, E. M. Souza, J. C. O. |
| description | 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. |
| doi_str_mv | 10.1007/s10589-022-00438-z |
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| source | Business Source Complete; SpringerNature Journals |
| subjects | 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 |
| title | A subgradient method with non-monotone line search |
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