Evaluation of scenario reduction algorithms with nested distance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10438220" target="_blank" >RIV/00216208:11320/20:10438220 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=LKIqvCHdf3" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=LKIqvCHdf3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10287-020-00375-4" target="_blank" >10.1007/s10287-020-00375-4</a>
Alternative languages
Result language
angličtina
Original language name
Evaluation of scenario reduction algorithms with nested distance
Original language description
Multistage stochastic optimization is used to solve many real-life problems where decisions are taken at multiple times. Such problems need the representation of stochastic processes, which are usually approximated by scenario trees. In this article, we implement seven scenario reduction algorithms: three based on random extraction, namedRandom, and four based on specific distance measures, named Distance-based. Three of the latter are well known in literature while the fourth is a new approach, namely nodal clustering. We compare all the algorithms in terms of computational cost and information cost. The computational cost is measured by the time needed for the reduction, while the information cost is measured by the nested distance between the original and the reduced tree. Moreover, we also formulate and solve a multistage stochastic portfolio selection problem to measure the distance between the optimal solutions and between the optimal objective values of the original and the reduced tree.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA18-05631S" target="_blank" >GA18-05631S: Stochastic optimization problems with endogenous uncertainty</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computational Management Science
ISSN
1619-697X
e-ISSN
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Volume of the periodical
17
Issue of the periodical within the volume
2
Country of publishing house
DE - GERMANY
Number of pages
35
Pages from-to
241-275
UT code for WoS article
000558873000001
EID of the result in the Scopus database
2-s2.0-85089288523