Importance sampling for stochastic programming
Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the exptected future costs of current decisions, often referred to as the recourse function. In practice, this...
Main Author: | Tran, Quang Kha |
---|---|
Other Authors: | Kuhn, Daniel ; Rustem, Berc |
Published: |
Imperial College London
2016
|
Subjects: | |
Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.700721 |
Similar Items
-
Problem-driven scenario generation for stochastic programs
by: Fairbrother, Jamie
Published: (2016) -
Interior point methods for nonlinear programming and application to mixed integer nonlinear programming
by: Akrotirianakis, Ioannis
Published: (2011) -
Computational methods for large-scale quadratic programming
by: Morales-Perez, Jose Luis
Published: (1993) -
The modelling and analysis of mathematical programming problems : tools and applications
by: DomiÌnguez-Ballesteros, MariÌa BeleÌn
Published: (2001) -
Contributions to Linear Programming Theory: Applications to Chebychev and Other Linear Approximation Criteria; The Transportation Problem; and the Problem of Resolution of Degeneracy in Linear programming Alyorithms
by: Appa, G. H.
Published: (1978)