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IOR
2011
152views more  IOR 2011»
13 years 5 months ago
Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
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 ...
Naomi Miller, Andrzej Ruszczynski
AMAI
2004
Springer
14 years 4 months ago
Approximate Probabilistic Constraints and Risk-Sensitive Optimization Criteria in Markov Decision Processes
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...
Dmitri A. Dolgov, Edmund H. Durfee
INFOCOM
2000
IEEE
14 years 3 months ago
Optimal Partition of QoS requirements with Discrete Cost Functions
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 ...
Danny Raz, Yuval Shavitt
NIPS
1996
13 years 12 months ago
Radial Basis Function Networks and Complexity Regularization in Function Learning
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...
Adam Krzyzak, Tamás Linder
CDC
2009
IEEE
379views Control Systems» more  CDC 2009»
14 years 2 months ago
Receding horizon cost optimization for overly constrained nonlinear plants
— 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...
David Angeli, Rishi Amrit, James B. Rawlings