We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Abstract—The future Internet is expected to support applications with quality of service (QoS) requirements. For this end several mechanisms are suggested in the IETF to support ...
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
— A receding horizon control algorithm, originally proposed for tracking best-possible steady-states in the presence of overly stringent state and/or input constraints, is analyz...