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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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