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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