In this paper, proper optimality concepts in vector optimization with variable ordering
structures are introduced for the first time and characterization results via scalarizations are …

In this paper we study optimality conditions for optimization problems described by a special
class of directionally differentiable functions. The well-known necessary and sufficient …

In this paper, we study the radial epiderivative notion for nonconvex functions, which
extends the (classical) directional derivative concept. The paper presents new definition and …

In this study, a new algorithm based on polyhedral conic func tions (PCFs) is developed to
solve multi-class supervised data classiffication problems. The k PCFs are constructed for …

In this paper, a piecewise linear classifier based on polyhedral conic separation is
developed. This classifier builds nonlinear boundaries between classes using polyhedral …

In this article, we introduce a notion of higher-order radial epiderivative for set-valued maps
and study its properties. A generalized concept of higher-order strict minimizers in set …

This paper presents a weak subgradient based method for solving nonconvex optimization
problems. The method uses a weak subgradient of the objective function at a current point to …

In this paper, a novel sharp Augmented Lagrangian-based global optimization method is
developed for solving constrained non-convex optimization problems. The algorithm …

In this paper, we study some characterizations of the class of weakly subdifferentiable
functions and formulate optimality conditions for nonconvex mathematical programming …

In this paper, zero duality gap conditions in nonconvex optimization are investigated. It is
considered that dual problems can be constructed with respect to the weak conjugate …