A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Aggregate subgradient method for nonsmooth DC optimization
Tekijät: Bagirov Adil M., Taheri Sona, Joki Kaisa, Karmitsa Napsu, Mäkelä Marko M.
Kustantaja: SPRINGER HEIDELBERG
Julkaisuvuosi: 2021
Journal: Optimization Letters
Tietokannassa oleva lehden nimi: OPTIMIZATION LETTERS
Lehden akronyymi: OPTIM LETT
Vuosikerta: 15
Aloitussivu: 83
Lopetussivu: 96
Sivujen määrä: 14
ISSN: 1862-4472
eISSN: 1862-4480
DOI: https://doi.org/10.1007/s11590-020-01586-z
Verkko-osoite: https://link.springer.com/article/10.1007/s11590-020-01586-z
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/48590187
The aggregate subgradient method is developed for solving unconstrained nonsmooth difference of convex (DC) optimization problems. The proposed method shares some similarities with both the subgradient and the bundle methods. Aggregate subgradients are defined as a convex combination of subgradients computed at null steps between two serious steps. At each iteration search directions are found using only two subgradients: the aggregate subgradient and a subgradient computed at the current null step. It is proved that the proposed method converges to a critical point of the DC optimization problem and also that the number of null steps between two serious steps is finite. The new method is tested using some academic test problems and compared with several other nonsmooth DC optimization solvers.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
