Thursday, 13:15 - 13:40 h, Room: MA 144

 

Anna Timonina
Multi-stage stochastic optimisation and approximations with applications

 

Abstract:
Multi-stage stochastic optimization problems play a very important role in management of financial portfolios, energy production, insurance portfolios etc. The exact analytical solution for such problems can be found only in very exceptional cases and the necessity of an approximation arises immediately. The aim of this research is to study the approximation of the stochastic process by the probability valued finite tree. We use the concept of nested distribution to describe the information structure keeping the setup purely distributional and the concept of nested distance to measure the distance between nested distributions and to quantify the quality of approximation. We introduce the algorithm for calculating the nested distance between tree and stochastic process given by its distribution. Minimization of this distance can lead to the new method for generating values from some specific distribution along with Monte Carlo generating and Optimal Quantization. The main advantage of this algorithm is that it takes into account conditional distributions at each stage, that allows to approximate a large class of processes.

 

Talk 1 of the contributed session Thu.2.MA 144
"Large-scale and multi-stage stochastic optimization" [...]
Cluster 22
"Stochastic optimization" [...]

 

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