A combinatorial arc tolerance analysis for network flow problems.
A compact variable metric algorithm for linearly constrained nonlinear minimax approximation
A comparative study of redundant constraints identification methods in linear programming problems.
A comparative study of three different mathematical methods for solving the unit commitment problem.
A comparative study on optimization methods for the constrained nonlinear programming problems.
A Comparison of Algorithms for the Maximum Clique Problem
A comparison of multiobjective evolutionary algorithms.
A comparison of solvers for linear complementarity problems arising from large-scale masonry structures
We compare the numerical performance of several methods for solving the discrete contact problem arising from the finite element discretisation of elastic systems with numerous contact points. The problem is formulated as a variational inequality and discretised using piecewise quadratic finite elements on a triangulation of the domain. At the discrete level, the variational inequality is reformulated as a classical linear complementarity system. We compare several state-of-art algorithms that have...
A complementarity approach for solving Leontief substitution systems and (generalized) Markov decision processes
A computational approach to pivot selection in the LP relaxation of set problems.
A computationally efficient stable dual-mode type nonlinear predictive control algorithm
A conjugate direction method for approximating the analytic center of a polytope.
A conjugate gradient method for unconstrained optimization problems.
A conjugate gradient method with quasi-Newton approximation
The conjugate gradient method of Liu and Storey is an efficient minimization algorithm which uses second derivatives information, without saving matrices, by finite difference approximation. It is shown that the finite difference scheme can be removed by using a quasi-Newton approximation for computing a search direction, without loss of convergence. A conjugate gradient method based on BFGS approximation is proposed and compared with existing methods of the same class.
A conjugate gradient method with sufficient descent and global convergence for unconstrained nonlinear optimization.
A Constraint Linearization Method for Nondifferentiable Convex Minimization.
A consumption-investment problem modelled as a discounted Markov decision process
In this paper a problem of consumption and investment is presented as a model of a discounted Markov decision process with discrete-time. In this problem, it is assumed that the wealth is affected by a production function. This assumption gives the investor a chance to increase his wealth before the investment. For the solution of the problem there is established a suitable version of the Euler Equation (EE) which characterizes its optimal policy completely, that is, there are provided conditions...
A Continuous Conditional Gradient Method
A continuous optimization model for a joint problem of pricing and resource allocation
This paper investigates the problem of maximizing the revenue of a telecommunications operator by simultaneously pricing point-to-point services and allocating bandwidth in its network, while facing competition. Customers are distributed into market segments, i.e., groups of customers with a similar preference for the services. This preference is expressed using utility functions, and customers choose between the offers of the operator and of the competition according to their utility. We model...
A continuum of minimal pairs of compact convex sets which are not connected by translations.