Approximation of multistage stochastic programming problems by smoothed quantization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00587649" target="_blank" >RIV/67985556:_____/24:00587649 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/24:10490667

Výsledek na webu

<a href="https://link.springer.com/content/pdf/10.1007/s11846-024-00733-5.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007/s11846-024-00733-5.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11846-024-00733-5" target="_blank" >10.1007/s11846-024-00733-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Approximation of multistage stochastic programming problems by smoothed quantization

Popis výsledku v původním jazyce

We present an approximation technique for solving multistage stochastic programming problems with an underlying Markov stochastic process. This process is approximated by a discrete skeleton process, which is consequently smoothed down by means of the original unconditional distribution. Approximated in this way, the problem is solvable by means of Markov Stochastic Dual Dynamic Programming. We state an upper bound for the nested distance between the exact process and its approximation and discuss its convergence in the one-dimensional case. We further propose an adjustment of the approximation, which guarantees that the approximate problem is bounded. Finally, we apply our technique to a reallife production-emission trading problem and demonstrate the performance of its approximation given the “true” distribution of the random parameters.

Název v anglickém jazyce

Approximation of multistage stochastic programming problems by smoothed quantization

Popis výsledku anglicky

We present an approximation technique for solving multistage stochastic programming problems with an underlying Markov stochastic process. This process is approximated by a discrete skeleton process, which is consequently smoothed down by means of the original unconditional distribution. Approximated in this way, the problem is solvable by means of Markov Stochastic Dual Dynamic Programming. We state an upper bound for the nested distance between the exact process and its approximation and discuss its convergence in the one-dimensional case. We further propose an adjustment of the approximation, which guarantees that the approximate problem is bounded. Finally, we apply our technique to a reallife production-emission trading problem and demonstrate the performance of its approximation given the “true” distribution of the random parameters.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA21-07494S" target="_blank" >GA21-07494S: Účinnost politiky snižování emisí uhlíku</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Review of Managerial Science

ISSN

1863-6683

e-ISSN

1863-6691

Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

36

Strana od-do

2079-2114

Kód UT WoS článku

001175189200001

EID výsledku v databázi Scopus

2-s2.0-85186174671