Approximation algorithms for integer covering problems via greedy column generation
Approximation algorithms for metric tree cover and generalized tour and tree covers
Given a weighted undirected graph G = (V,E), a tree (respectively tour) cover of an edge-weighted graph is a set of edges which forms a tree (resp. closed walk) and covers every other edge in the graph. The tree (resp. tour) cover problem is of finding a minimum weight tree (resp. tour) cover of G. Arkin, Halldórsson and Hassin (1993) give approximation algorithms with factors respectively 3.5 and 5.5. Later Könemann, Konjevod, Parekh, and Sinha (2003) study the linear programming relaxations...
Approximation algorithms for the design of SDH/SONET networks
In this paper, a graph partitioning problem that arises in the design of SONET/SDH networks is defined and formalized. Approximation algorithms with performance guarantees are presented. To solve this problem efficiently in practice, fast greedy algorithms and a tabu-search method are proposed and analyzed by means of an experimental study.
Approximation algorithms for the design of SDH/SONET networks
In this paper, a graph partitioning problem that arises in the design of SONET/SDH networks is defined and formalized. Approximation algorithms with performance guarantees are presented. To solve this problem efficiently in practice, fast greedy algorithms and a tabu-search method are proposed and analyzed by means of an experimental study.
Approximation and adaptive control of Markov processes: Average reward criterion
Approximation and estimation in Markov control processes under a discounted criterion
We consider a class of discrete-time Markov control processes with Borel state and action spaces, and -valued i.i.d. disturbances with unknown density Supposing possibly unbounded costs, we combine suitable density estimation methods of with approximation procedures of the optimal cost function, to show the existence of a sequence of minimizers converging to an optimal stationary policy
Approximation des optima de Pareto
Approximation, estimation and control of stochastic systems under a randomized discounted cost criterion
The paper deals with a class of discrete-time stochastic control processes under a discounted optimality criterion with random discount rate, and possibly unbounded costs. The state process and the discount process evolve according to the coupled difference equations
Approximation of a random solution in extremum problems
Approximation of Optimal Distributed Control Problems Governed by Variational Inequalities.
Approximation of phenol concentration using novel hybrid computational intelligence methods
Approximation of set-valued functions by single-valued one
Approximation of the pareto optimal set for multiobjective optimal control problems using viability kernels
This paper provides a convergent numerical approximation of the Pareto optimal set for finite-horizon multiobjective optimal control problems in which the objective space is not necessarily convex. Our approach is based on Viability Theory. We first introduce a set-valued return function V and show that the epigraph of V equals the viability kernel of a certain related augmented dynamical system. We then introduce an approximate set-valued return function with finite set-values as the solution of...
Approximation Results Toward Nearest Neighbor Heuristic
Approximations for Markov decision problems
Approximations to Stochastic Programs with Complete Fixed Recourse.
Approximative solutions of stochastic optimization problems
The aim of this paper is to present some ideas how to relax the notion of the optimal solution of the stochastic optimization problem. In the deterministic case, -minimal solutions and level-minimal solutions are considered as desired relaxations. We call them approximative solutions and we introduce some possibilities how to combine them with randomness. Relations among random versions of approximative solutions and their consistency are presented in this paper. No measurability is assumed, therefore,...
Asignación de recursos Max-Min: propiedades y algoritmos.
Este trabajo trata el problema de asignación de recursos cuando el objetivo es maximizar la mínima recompensa y las funciones recompensa son continuas y estrictamente crecientes. Se estudian diferentes propiedades que conducen a algoritmos que permiten de forma eficiente la resolución de gran variedad de problemas de esta naturaleza, tanto con variables continuas como discretas.
Aspects of control for the normal Markov processes.
Assignment problem with fuzzy estimates of effectiveness