Generalized invexity on differentiable manifolds.
Generalized Levitin-Polyak well-posedness of vector equilibrium problems.
Generalized minimizers of convex integral functionals, Bregman distance, Pythagorean identities
Integral functionals based on convex normal integrands are minimized subject to finitely many moment constraints. The integrands are finite on the positive and infinite on the negative numbers, strictly convex but not necessarily differentiable. The minimization is viewed as a primal problem and studied together with a dual one in the framework of convex duality. The effective domain of the value function is described by a conic core, a modification of the earlier concept of convex core. Minimizers...
Generalized monotone nonsmooth maps.
Generalized Newton and NCP-methods: convergence, regularity, actions
Solutions of several problems can be modelled as solutions of nonsmooth equations. Then, Newton-type methods for solving such equations induce particular iteration steps (actions) and regularity requirements in the original problems. We study these actions and requirements for nonlinear complementarity problems (NCP's) and Karush-Kuhn-Tucker systems (KKT) of optimization models. We demonstrate their dependence on the applied Newton techniques and the corresponding reformulations. In this way, connections...
Generalized nonlinear implicit quasivariational inclusions.
Generalized nonlinear variational inequality problems involving multivalued mappings.
Generalized optimal search paths for continuous univariate random variables
Generalized ordering policies with general random minimal repair costs and random lead times
Generalized second-order mixed symmetric duality in nondifferentiable mathematical programming.
Generalized strongly set-valued nonlinear complementarity problems.
Generalized truncated methods for an efficient solution of retrial systems.
Generalized vector complementarity problems with moving cones.
Generalized vector quasi-equilibrium problems with set-valued mappings.
Generic Primal-dual Interior Point Methods Based on a New Kernel Function
In this paper we present a generic primal-dual interior point methods (IPMs) for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. The proposed kernel function does not satisfy all the conditions proposed in [2]. We show that the corresponding large-update algorithm improves the iteration complexity with a factor when compared with the method based on the use of the classical...
Generic well-posedness for a class of equilibrium problems.
Generic well-posedness in minimization problems.
Generic well-posedness in some classes of optimization problems
Genetic Algorithm Approach for Solving the Task Assignment Problem
This research was partially supported by the Serbian Ministry of Science and Ecology under project 144007. The authors are grateful to Ivana Ljubić for help in testing and to Vladimir Filipović for useful suggestions and comments.In this paper a genetic algorithm (GA) for the task assignment problem (TAP) is considered.An integer representation with standard genetic operators is used. Computational results are presented for instances from the literature, and compared to optimal solutions obtained...
Genetic algorithm based approach to bi-level linear programming