In this paper we propose new efficient gradient schemes for two non-trivial classes of linear programming problems. These schemes are designed to compute approximate solutions withrelative accuracy . We prove that the upper complexity bound for both ln schemes is O( n m ln n) iterations of a...

In this paper we present a new approach to constructing schemes for unconstrained convex minimization, which compute approximate solutions with a certain relative accuracy. This approach is based on a special conic model of the unconstrained minimization problem. Using a structural model of the...

We consider a choice-based, network revenue management (RM) problem in a setting where heterogeneous customers consider an assortment of products offered by a firm (e.g., different flight times, fare classes, and/or routes). Individual choice decisions are modeled through an ordered list of...

Through Monte Carlo experiments, this paper compares the performances of different gradient optimization algorithms, when performing full information maximum likelihood (FIML) estimation of econometric models. Different matrices are used (Hessian, outer products matrix, GLS-type matrix, as well...

Full information maximum likelihood estimation of econometric models, linear and nonlinear in variables, is performed by means of two gradient algorithms, using either the Hessian matrix or a computationally simpler approximation. In the first part of the paper, the behavior of the two methods...