The routing capacity region of networks with multiple unicast sessions can be characterized using Farkas lemma as an infinite set of linear inequalities. In this paper this result is sharpened by exploiting properties of the solution satisfied by each rate-tuple on the boundary of the capacity...

This paper analyzes numerically a long-term average stochastic control problem involving a controlled diffusion on a bounded region. The solution technique takes advantage of an infinite-dimensional linear programming formulation for the problem which relates the stationary measures to the...

We consider a discrete time Markov Decision Process (MDP) under the discounted payoff criterion in the presence of additional discounted cost constraints. We study the sensitivity of optimal Stationary Randomized (SR) policies in this setting with respect to the upper bound on the discounted...

In this paper, we introduce a one-parametric class of smoothing functions which contains the Fischer–Burmeister smoothing function and the CHKS smoothing function as special cases. Based on this class of smoothing functions, a smoothing Newton algorithm is extended to solve linear programming...

We propose an optimal schedule for multiple classes of arrivals in a queueing system consisting of queues in tandem. The arrival process for each class is Poisson with different rates, and the service times are constant. A theoretical result is presented by Linear Programming of sample-path...

In this paper, an extended form of the entropic perturbation method of linear programming is given, which can overcome the weakness of the original method – being easy of overflow in computing. Moreover, the global convergence of the gradient algorithm for the method is discussed. Copyright...

This paper uses linear programming to numerically evaluate the Laplace transform of the exit time distribution and the resolvent of the moments of various Markov processes in bounded regions. The linear programming formulation is developed from a martingale characterization of the processes and...

We consider zero-sum Markov games with incomplete information. Here, the second player is never informed about the current state of the underlying Markov chain. The existence of a value and of optimal strategies for both players is shown. In particular, we present finite algorithms for computing...

We consider discrete time Markov Decision Process (MDP) with finite state and action spaces under average reward optimality criterion. The decomposition theory, in Ross and Varadarajan [11], leads to a natural partition of the state space into strongly communicating classes and a set of states...

This paper describes theNeighbourhood Search, an effectivemethod that we suggest for constructing Pareto sets in multiple objective problems with conegenerated orders. TheNeighbourhood Search is then applied to discounted Markov Decision Processes, resulting in original statements about...