0000000000184254
AUTHOR
E. Vercher
Comparacion numerica de algoritmos para calcular distribuciones estacionarias de cadenas de Markov finitas
En este trabajo se estudia la eficiencia de un conjunto de algoritmos, exactos e iterativos, para el problema de obtener la distribucion estacionaria de una cadena de Markov homogenea, irreducible y finita. Se presentan los resultados computacionales obtenidos al resolver problemas de diferentes tipos y tamanos, aleatoriamente generados, asi como el tratamiento estadistico realizado sobre los mismos. Se ha comparado la estabilidad de estos algoritmos frente a la perdida de irreducibilidad y la existencia de estados transitorios mediante su aplicacion a 26 problemas test. El trabajo concluye con una discusion del comportamiento de los diversos algoritmos.
An optimality test for semi-infinite linear programming
In this paper we present a test to characterize the optimal solutions for the continuous semi-infinite linear programming problem. This optimality characterization is a condition of Kuhn–Tucker type. The resolution of a linear program permits to check the optimality of a feasible point,to detect the unboundedness of the problem and to find descent directions. We give some illustrative examples. We show that the local Mangasarian–Fromovitz constraint qualification is almost equivalent to Slater qualification for this problem. Furthermore, it follows from our study that this optimality condition is always necessary for a wide class of semi-infinite linear programming problems
An overview of semi-infinite programming theory and related topics through a generalization of the alternative theorems
We propose new alternative theorems for convex infinite systems which constitute the generalization of the corresponding toGale, Farkas, Gordan andMotzkin. By means of these powerful results we establish new approaches to the Theory of Infinite Linear Inequality Systems, Perfect Duality, Semi-infinite Games and Optimality Theory for non-differentiable convex Semi-Infinite Programming Problem.