Numerical study of discretizations of multistage stochastic programs
Kybernetika (2008)
- Volume: 44, Issue: 2, page 185-204
 - ISSN: 0023-5954
 
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topHilli, Petri, and Pennanen, Teemu. "Numerical study of discretizations of multistage stochastic programs." Kybernetika 44.2 (2008): 185-204. <http://eudml.org/doc/33921>.
@article{Hilli2008,
	abstract = {This paper presents a numerical study of a deterministic discretization procedure for multistage stochastic programs where the underlying stochastic process has a continuous probability distribution. The discretization procedure is based on quasi-Monte Carlo techniques originally developed for numerical multivariate integration. The solutions of the discretized problems are evaluated by statistical bounds obtained from random sample average approximations and out-of-sample simulations. In the numerical tests, the optimal values of the discretizations as well as their first-stage solutions approach those of the original infinite-dimensional problem as the discretizations are made finer.},
	author = {Hilli, Petri, Pennanen, Teemu},
	journal = {Kybernetika},
	keywords = {stochastic programming; discretization; integration quadratures; simulation; stochastic programming; discretization; integration quadratures; simulation},
	language = {eng},
	number = {2},
	pages = {185-204},
	publisher = {Institute of Information Theory and Automation AS CR},
	title = {Numerical study of discretizations of multistage stochastic programs},
	url = {http://eudml.org/doc/33921},
	volume = {44},
	year = {2008},
}
TY  - JOUR
AU  - Hilli, Petri
AU  - Pennanen, Teemu
TI  - Numerical study of discretizations of multistage stochastic programs
JO  - Kybernetika
PY  - 2008
PB  - Institute of Information Theory and Automation AS CR
VL  - 44
IS  - 2
SP  - 185
EP  - 204
AB  - This paper presents a numerical study of a deterministic discretization procedure for multistage stochastic programs where the underlying stochastic process has a continuous probability distribution. The discretization procedure is based on quasi-Monte Carlo techniques originally developed for numerical multivariate integration. The solutions of the discretized problems are evaluated by statistical bounds obtained from random sample average approximations and out-of-sample simulations. In the numerical tests, the optimal values of the discretizations as well as their first-stage solutions approach those of the original infinite-dimensional problem as the discretizations are made finer.
LA  - eng
KW  - stochastic programming; discretization; integration quadratures; simulation; stochastic programming; discretization; integration quadratures; simulation
UR  - http://eudml.org/doc/33921
ER  - 
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