Extension principle and fuzzy-mathematical programming
Fast computation of the leastcore and prenucleolus of cooperative games
The computation of leastcore and prenucleolus is an efficient way of allocating a common resource among n players. It has, however, the drawback being a linear programming problem with 2n - 2 constraints. In this paper we show how, in the case of convex production games, generate constraints by solving small size linear programming problems, with both continuous and integer variables. The approach is extended to games with symmetries (identical players), and to games with partially continuous...
Finding target units in FDH model by least-distance measure model
Recently, some authors used the Least-Distance Measure model in order to obtain the shortest distance between the evaluated Decision Making Unit (DMU) and the strongly efficient production frontier. But, their model is not applicable for situation in which the production possibility set satisfies free disposability property. In this paper, we propose a new approach to this end in FDH model which improves the application potential of the Least-Distance Measure and overcomes the mentioned shortcoming....
Fixed cost minimization over a Leontief substitution system
Flot à coût convexe linéaire par morceaux
Fondements, généralisation et critique de la notion d'affinité (Problème du voyageur de commerce)
Formalisation et résolution des problèmes de découpes linéaires
Fourier analysis, linear programming, and densities of distance avoiding sets in
We derive new upper bounds for the densities of measurable sets in which avoid a finite set of prescribed distances. The new bounds come from the solution of a linear programming problem. We apply this method to obtain new upper bounds for measurable sets which avoid the unit distance in dimensions . This gives new lower bounds for the measurable chromatic number in dimensions . We apply it to get a short proof of a variant of a recent result of Bukh which in turn generalizes theorems of Furstenberg,...
Full-Newton step infeasible interior-point algorithm for SDO problems
In this paper we propose a primal-dual path-following interior-point algorithm for semidefinite optimization. The algorithm constructs strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Each main step of the algorithm consists of a feasibility step and several centering steps. At each iteration, we use only full-Newton step. Moreover, we use a more natural feasibility step, which targets at the -center. The iteration bound of the algorithm coincides...
GAMS langauage in description of method of minimum total variation.
General Parametric Linear Fractional Programming.
Generalización de los teoremas de alternativa de sistemas de inecuaciones lineales.
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...
Genetic algorithm based approach to bi-level linear programming
Geometric algorithms and combinatorial optimization [Book]
Geometric algorithms and combinatorial optimization [Book]
Geometry of the Gass-Saaty Parametric Cost LP Algorithm.
Graph-based upper bounds for the probability of the union of events.
Helly-Type Theorems and Generalized Linear Programming.
Heurísticas de descomposición lagrangiana para algunos problemas de localización discreta.
En este trabajo se considera el Problema de Localización de Plantas Simple y el Problema de la p-Mediana Generalizado. Se construyen dos algoritmos heurísticos, uno para cada problema, basados en una técnica de descomposición lagrangiana para problemas binarios. Los algoritmos son implementados en un microordenador y ejecutados sobre una serie de problemas generados aleatoriamente. Los resultados computacionales son comparados con los de otros dos algoritmos heurísticos basados en la optimización...