In this paper, we first extend the diminishing stepsize method for nonconvex constrained problems presented in F. Facchinei, V. Kungurtsev, L. Lampariello and G. Scutari [Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity, To appear on Math. Oper. Res. 2020. Available at https://arxiv.org/abs/1709.03384.] to deal with equality constraints and a nonsmooth objective function of composite type. We then consider the particular case in which the constraints are convex and satisfy a standard constraint qualification and show that in this setting the algorithm can be considerably simplified, reducing the computational burden of each iteration.
Facchinei, F., Kungurtsev, V., Lampariello, L., & Scutari, G. (2020). Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. OPTIMIZATION METHODS & SOFTWARE, 1-27 [10.1080/10556788.2020.1854253].
Titolo: | Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case | |
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Data di pubblicazione: | 2020 | |
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Citazione: | Facchinei, F., Kungurtsev, V., Lampariello, L., & Scutari, G. (2020). Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. OPTIMIZATION METHODS & SOFTWARE, 1-27 [10.1080/10556788.2020.1854253]. | |
Handle: | http://hdl.handle.net/11590/379361 | |
Appare nelle tipologie: | 1.1 Articolo in rivista |