Two Algorithms for Risk-averse Reformulation of Multi-stage Stochastic Programming Problems

0493316 - ÚTIA 2019 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Šmíd, Martin - Kozmík, Václav
Two Algorithms for Risk-averse Reformulation of Multi-stage Stochastic Programming Problems.
36th International Conference Mathematical Methods in Economics. Praha: MatfyzPress, 2018 - (Váchová, L.; Kratochvíl, V.), s. 551-554. ISBN 978-80-7378-371-6.
[36th International Conference Mathematical Methods in Economics. Jindřichův Hradec (CZ), 12.09.2018-14.09.2018]
Grant CEP: GA ČR(CZ) GA16-01298S
Institucionální podpora: RVO:67985556
Klíčová slova: Multi-stage stochastic programming * deterministic equivalent * multi-period CVaR * nested CVaR * optimization algorithm
Obor OECD: Economic Theory
http://library.utia.cas.cz/separaty/2018/E/smid-0493316.pdf

Many real-life applications lead to risk-averse multi-stage stochastic problems, therefore effective solution of these problems is of great importance. Many tools can be used to their solution (GAMS, Coin-OR, APML or, for smaller problems, Excel), it is, however, mostly up to researcher to reformulate the problem into its deterministic equivalent. Moreover, such solutions are usually one-time, not easy to modify for different applications. We overcome these problems by providing a front-end software package, written in C++, which enables to enter problem definitions in a way close to their mathematical definition. Creating of a deterministic equivalent (and its solution) is up to the computer. In particular, our code is able to solve linear multi-stage with Multi-period Mean-CVaR or Nested Mean-CVaR criteria. In the present paper, we describe the algorithms, transforming these problems into their deterministic equivalents.
Trvalý link: http://hdl.handle.net/11104/0286991