Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. (English) Zbl 1509.90153
Summary: In this paper, we first extend the diminishing stepsize method for nonconvex constrained problems presented in [F. Facchinei et al., Math. Oper. Res. 46, No. 2, 595–627 (2021; Zbl 1471.90137)] 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.
MSC:
| 90C26 | Nonconvex programming, global optimization |
Keywords:
constrained optimization; nonconvex optimization; composite optimization; diminishing stepsizeCitations:
Zbl 1471.90137References:
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