In this paper we develop a new primal-dual subgradient method for nonsmooth convex optimization problems. This scheme is based on a self-concordant barrier for the basic feasible set. It is suitable for finding approximate solutions with certain relative accuracy. We discuss some applications of...

METROPOLIS proposes an interactive environment which simulates automobile traffic in large urban areas. The core of the system is a dynamic simulator ehich integrates commuters' departure time and route choice behavirs over large networks: Drivers are assumed to minimize a generalized travel...

In this paper we present several "infeasible-start" path-following and potential-reduction primal-dual interior-point methods for non-linear conic problems. These methods try to find a recession direction of the feasible set of a self-dual homogeneous primal-dual problem.

In this paper we study the concepts of equilibrium and optimum in static transportation networks with elastic and non-elastic demands. The main mathematical tool of our paper is the theory of variational inequalities. We demonstrate that this theory is useful for proving the existence theorems....

In this paper we consider a new analytic center cutting plane method in a projective space. We prove the efficiency estimates for the general schemeand show that these results can be used in the analysis of a feasibility problem, the variational inequality problem and the problem of constrained...

We present a new class of transportation systems, the stable dynamics models, which provides a natural link between the static and dynamic traffic network models. They can be seen as steady states of dynamic networks (flows are constant in time). These models turn out to be very easy to study...

We propose an alternative apporach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The associated objectiev value is doubly random, since it depends two...

In the first part of this paper we prove that the global quadratic optimization problem over a simplex can be solved with a constant relative accuracy. In the second part we consider some natural extensions of the result.