Semistrictly and neatly quasiconvex programming using lower global subdifferentials

A. Kabgani () and F. Lara ()
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A. Kabgani: University of Antwerp
F. Lara: University of Tarapacá

Journal of Global Optimization, 2023, vol. 86, issue 4, No 2, 845-865

Abstract: Abstract The main goal of this paper is to investigate the properties and connections of neatly and semistrictly quasiconvex functions, especially when they appear in constrained and unconstrained optimization problems. The lower global subdifferential, recently introduced in the literature, plays an essential role in this study. We present several optimality conditions for constrained and unconstrained nonsmooth neatly/semistrictly quasiconvex optimization problems in terms of lower global subdifferentials. To this end, for a constrained optimization problem, we present some characterizations for the normal and tangent cones and the cone of feasible directions of the feasible set. Some relationships between the Greenberg–Pierskalla, tangentially and lower global subdifferentials of neatly and semistrictly quasiconvex functions are also given. The mentioned relationships show that the outcomes of this paper generalize some results existing in the literature.

Keywords: Nonconvex optimization; Quasiconvex programming; KKT conditions; Global subdifferentials; Greenberg–Pierskalla’s subdifferential (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10898-023-01278-9

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