An Algorithm for Discrete Linear Lp Approximation.
An Algorithm for Finding a Cycle of Fields in a Matrix
An algorithm for indefinite quadratic programming based on a partial Cholesky factorization
An Algorithm for Optimization
An Algorithm for M Asymmetric Travelling Salesman Problem on a Bandwidth-Limited Graph
An algorithm for multiparametric 0-1-Integer Programming problems relative to a generalized min max objective function
The multiparametric 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete multiparametric analysis relative to a generalized min max objective function such that the min sum and min max are particular cases.
An algorithm for multiparametric min max 0-1-integer programming problems relative to the objective function
The multiparametric min max 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of min max 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete multiparametric analysis relative to the objective function.
An algorithm for multiparametric min max 0-1-integer programming problems relative to the objective function
The multiparametric min max 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of min max 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete multiparametric analysis relative to the objective function.
An algorithm for solving multiple objective integer linear programming problem
In the present paper a complete procedure for solving Multiple Objective Integer Linear Programming Problems is presented. The algorithm can be regarded as a corrected form and an alternative to the method that was proposed by Gupta and Malhotra. A numerical illustration is given to show that this latter can miss some efficient solutions. Whereas, the algorithm stated bellow determines all efficient solutions without missing any one.
An Algorithm For Solving Multiple Objective Integer Linear Programming Problem
In the present paper a complete procedure for solving Multiple Objective Integer Linear Programming Problems is presented. The algorithm can be regarded as a corrected form and an alternative to the method that was proposed by Gupta and Malhotra. A numerical illustration is given to show that this latter can miss some efficient solutions. Whereas, the algorithm stated bellow determines all efficient solutions without missing any one.
An algorithm for the minimum variance point of a network
An algorithm of successive minimization in convex programming
An Alternative Computational Method for the Approximation of Random Functions.
An analysis of low-rank modifications of preconditioners for saddle point systems.
An analytic center cutting plane algorithm for finding equilibrium points
We present a variant of the analytic center cutting plane algorithm proposed by Goffin et al. (1996) to approximately solve equilibrium problems as proposed by Blum and Oettli (1994), which include as particular problems the variational inequalities problem, the Nash equilibria problem in non-cooperative games, the convex minimization problem, and the fixed point problem. Furthermore, we analyze the convergence and complexity of the modified algorithm.
An application of conjugate duality for numerical solution of continuous convex optimal control problems
An application of semi-infinite linear programming: approximation of a continuous function by a polynomial
An application of the Hahn-Banach theorem in convex analysis
An approach to finding trade-off solutions by a linear transformation of objective functions
An approach to robust network design in telecommunications
In telecommunications network design, one of the most frequent problems is to adjust the capacity on the links of the network in order to satisfy a set of requirements. In the past, these requirements were demands based on historical data and/or demographic predictions. Nowadays, because of new technology development and customer movement due to competitiveness, the demands present considerable variability. Thus, network robustness w.r.t demand uncertainty is now regarded as a major consideration....