Webpage dedicated to the booklet "On Selected Software for Stochastic Programming" HERE.
Seminar programme will be consequently updated. Guests are welcomed.
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Seminar is cancelled
- Author:
- Date:
- 22.2.2018
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Introductory seminar
- Author:
- doc. RNDr. Ing. Miloš Kopa, Ph.D.
- Date:
- 1.3.2018
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tbs
- Author:
- doc. RNDr. Ing. Miloš Kopa, Ph.D.
- Date:
- 8.3.2018
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tbs
- Author:
- Sebastiano Vitali, Ph.D.
- Date:
- 15.3.2018
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tbs
- Author:
- RNDr. Martin Šmíd, Ph.D.
- Date:
- 22.3.2018
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Seminar is cancelled - Easter holidays
- Author:
- Date:
- 29.3.2018
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Guaranteed Bounds for Multistage Risk-Averse Stochastic Optimization Programs
- Author:
- Assoc. Prof. Francesca Maggioni
- Date:
- 5.4.2018
- Abstract:
- In general, multistage stochastic optimization problems are formulated on
the basis of continuous distributions describing the uncertainty. Such
"infinite" problems are practically impossible to solve as they are
formulated and finite tree approximations of the underlying stochastic
processes are used as proxies.
In this talk bounding methods for multistage stochastic optimization
problems are discussed.
First we consider bounds based on the assumption that a sufficiently large
discretized scenario tree describing the problem uncertainty is given but
is unsolvable. Monotonic bounds based on group subproblems of the large
scenario tree will be discussed and compared in terms of computational
complexity.
Secondly, we demonstrate how one can find guaranteed bounds, i.e. finite
tree models, for which the optimal values give upper and lower bounds for
the optimal values of the original infinite problem. We consider
approximations in the first order stochastic sense, in the convex order
sense and based on subgradient approximations. Their use is shown in a
multistage risk-averse production problem.
Work done in collaboration with Prof. Georg Pflug (University of Vienna).
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tbs
- Author:
- RNDr. Michal Houda, Ph.D.
- Date:
- 12.4.2018
-
tbs
- Author:
- Prof. Georgio Consigli
- Date:
- 19.4.2018
- Abstract:
- We consider a canonical asset-liability management (ALM) model for a defined benefit pension fund from the perspective of a PF manager seeking an optimal dynamic investment strategy under a set of asset and liability constraints and in particular a chance constraint on the pension fund solvency condition. This class of problem is well-known and it has been studied under several modeling approaches, and specifically within a discrete framework through multistage stochastic programming (MSP). A real-world case-study has been presented with a detailed problem formulation and MSP solution approach in {Consigli et al. 2017: as in what follows, the complexity of such problem class comes from its long-term nature and the underlying risk sources, affecting asset returns and liability flows. In a MSP framwork those uncertainties require a dedicated statistical model from which a scenario tree process is derived. When, as mostly the case, asset returns and liability costs are assumed to carry a continuous probability space, approximate solutions can be obtained by substituting those probability distribution with a discrete approximation and allowing strategy revisions only at discrete time points. In presence of realistic PF ALM problems' instances MSP approaches are able to accommodate a rich set of assumptions and market details but at the cost of a possible curse of dimensionalty, the problem's in-sample instability and significant model risk: the first two represent a non trivial trade-off, since in-sample stability calls for robust and stable solutions to different, sufficiently rich sampling methods. The latter may lead to inefficient decision processes due to unsuitable statistical assumptions. These drawbacks may all be overcome through a distributonally robust optimization (DRO) approach, that can be regarded as a natural generalization of stochastic programming and robust optimization approaches, accounting for both the decision maker's attitude to risk and ambiguity: the latter being refered to the uncertainty characterizing the probability measure to be associated with the decision problem's underlying stochastic factors.
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tbs
- Author:
- Ing. Mgr. Barbora Petrová
- Date:
- 26.4.2018
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Optimal Loan Performance Management via Stochasic Programming
- Autor:
- Mgr. Tomáš Rusý
- Datum:
- 3.5.2018
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tbs
- Autor:
- RNDr. Vlasta Kaňková, CSc.
- Datum:
- 10.5.2018
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Seminar is cancelled, Kaunas conference
- Author:
- Date:
- 17.5.2018
-
Seminar is cancelled
- Author:
- Date:
- 24.5.2018