Approximate Reasoning Based Optimization
Approximate solution of stochastic programming problems with recourse
Approximating the solution of a dynamic, stochastic multiple knapsack problem
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 by finitely supported measures
We consider the problem of approximating a probability measure defined on a metric space by a measure supported on a finite number of points. More specifically we seek the asymptotic behavior of the minimal Wasserstein distance to an approximation when the number of points goes to infinity. The main result gives an equivalent when the space is a Riemannian manifold and the approximated measure is absolutely continuous and compactly supported.
Approximation by finitely supported measures
We consider the problem of approximating a probability measure defined on a metric space by a measure supported on a finite number of points. More specifically we seek the asymptotic behavior of the minimal Wasserstein distance to an approximation when the number of points goes to infinity. The main result gives an equivalent when the space is a Riemannian manifold and the approximated measure is absolutely continuous and compactly supported.
Approximation by finitely supported measures
We consider the problem of approximating a probability measure defined on a metric space by a measure supported on a finite number of points. More specifically we seek the asymptotic behavior of the minimal Wasserstein distance to an approximation when the number of points goes to infinity. The main result gives an equivalent when the space is a Riemannian manifold and the approximated measure is absolutely continuous and compactly supported.
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 nonconical preference relations in multiple-criteria decision problems.
Our work field is Multiple-Criteria Decision Making Problems. We study the binary relations, not necessarily conical, that represent the decisor's preferences in the Objective or Outcome Space, we approach them by using cones and we explore under what conditions this approximation can retrieve the entire information of these binary relations.
Approximation of Optimal Distributed Control Problems Governed by Variational Inequalities.
Approximation of phenol concentration using novel hybrid computational intelligence methods
Approximation of reliability for a large system with non-markovian repair-times
Approximation of Reliability for a large system with non-markovian repair-times
Consider a system of many components with constant failure rate and general repair rate. When all components are reliable and easily reparable, the reliability of the system can be evaluated from the probability q of failure before restoration. In [14], authors give an asymptotic approximation by monotone sequences. In the same framework, we propose, here, a bounding for q and apply it in the ageing property case.