Stránka o brožuře věnované vybraným softwarům pro řešení úloh stochastického programování ZDE.
Program bude průběžně doplňován. Hosté jsou srdečně zváni.

Seminář se nekoná
 Autor:
 Datum:
 22.2.2018

Úvodní přednáška
 Autor:
 doc. RNDr. Ing. Miloš Kopa, Ph.D.
 Datum:
 1.3.2018

Portfolio Optimization with DARA Stochastic Dominance Constraints
 Autor:
 doc. RNDr. Ing. Miloš Kopa, Ph.D.
 Datum:
 8.3.2018

Multistage multivariate nested distance: stage and scenario reduction
 Autor:
 Sebastiano Vitali, Ph.D.
 Datum:
 15.3.2018
 Abstract:
 Multistage stochastic optimization requires the definition and the generation of a discrete stochastic tree that represents the evolution of the uncertain parameters through the time and the space. The dimension of the tree is the results of a tradeoff between adaptability to the original probability distribution and computational tractability. Therefore, the recent literature investigates the concept of distance between trees which are candidate to be adopted as stochastic framework for the multistage model optimization. The contribution of this paper is to compute the nested distance between a large set of multistage and multivariate trees and, for a sample of basics financial problem, to empirically show the positive relation between the tree distance and the distance between the corresponding optimal solutions and the optimal objective values. Moreover, analyse the loss (measured in term of nested distance) due to a scenario reduction or to a stage reduction.

MS++: Library for Riskaverse Multistage Stochastic Programming
 Autor:
 RNDr. Martin Šmíd, Ph.D.
 Datum:
 22.3.2018

Seminář se nekoná  Velikonoce
 Autor:
 Datum:
 29.3.2018

Guaranteed Bounds for Multistage RiskAverse Stochastic Optimization Programs
 Autor:
 Assoc. Prof. Francesca Maggioni
 Datum:
 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 riskaverse production problem.
Work done in collaboration with Prof. Georg Pflug (University of Vienna).

tbs
 Autor:
 RNDr. Michal Houda, Ph.D.
 Datum:
 12.4.2018

Distributionally robust chanceconstrained dynamic pension fund management
 Autor:
 Prof. Giorgio Consigli
 Datum:
 19.4.2018
 Abstract:
 We consider a canonical assetliability 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 wellknown and it has been studied under several modeling approaches, and specifically within a discrete framework through multistage stochastic programming (MSP). A realworld casestudy 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 longterm 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 insample instability and significant model risk: the first two represent a non trivial tradeoff, since insample 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.

tbs
 Autor:
 RNDr. Mgr. Barbora Petrová
 Datum:
 26.4.2018

Optimal Loan Performance Management via Stochasic Programming
 Autor:
 Mgr. Tomáš Rusý
 Datum:
 3.5.2018

tbs
 Autor:
 RNDr. Vlasta Kaňková, CSc.
 Datum:
 10.5.2018

Seminář se nekoná  Konference Kaunas
 Autor:
 Datum:
 17.5.2018

Seminář se nekoná
 Autor:
 Datum:
 24.5.2018