In this paper, an augmented Lagrangian proximal alternating (ALPA) method is proposed for two class of large-scale sparse discrete constrained optimization problems in which a sequence of augmented Lagrangian subproblems are solved by utilizing proximal alternating linearized minimization framework and sparse projection techniques. Under the MangasarianFromovitz and the basic constraint qualifi...

It is known that the Karush-Kuhn-Tucker (KKT) conditions of semidefinite programming can be reformulated as a nonsmooth system via the metric projector over the cone of symmetric and positive semidefinite matrices. We show in this paper that the primal and dual constraint nondegeneracies, the strong regularity, the nonsingularity of the B-subdifferential of this nonsmooth system, and the nonsin...

This chapter is mainly about William Karush and his role in the Karush-KuhnTucker theorem of nonlinear programming. It tells the story of fundamental optimization results that he obtained in his master’s thesis: results that he neither published nor advertised and that were later independently rediscovered and published by Harold W. Kuhn and Albert W. Tucker. The principal result – which concer...

In this article, we view the Pareto and weak solutions of multiobjective optimization by using an approximate version KKT type conditions. one our main results Ekeland’s variational principle for vector-valued maps plays a key role. We also focus on improved Geoffrion proper it’s approximation characterize them through saddle point

In the traditional nonlinear optimization theory, Karush-Kuhn-Tucker (KKT) optimality conditions for constrained problems with inequality constraints play an essential role. The situation becomes challenging when theory of is discussed under uncertainty. Several researchers have interval approach to tackle uncertainty and derived conditions. However, there are several realistic situations in wh...

In this paper, the common centralized DEA models are extended to the bi-level centralized resource allocation (CRA) models based on revenue efficiency. Based on the Karush–Kuhn–Tucker (KKT) conditions, the bi-level CRA model is reduced to a one-level mathematical program subject to complementarity constraints (MPCC). A recurrent neural network is developed for solving this one-level mathematica...