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National Repository of Grey Literature 14 records found  previous11 - 14  jump to record: Search took 0.01 seconds. 
Porovnání aproximací v úlohách stochastické a robustní optimalizace
 Houda, Michal
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We specialize to different approaches when solving an optimization problem where some uncertainties in constraints occur. To overcome uncertainty, we can request the solution to be feasible to all but a small part of constraints. Both approaches gives us different methods to deal with this requirement. We try to find fundamental differencies between them and illustrate the differencies on a simple numerical example.
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Aproximace stochastických a robustních optimalizačních úloh
 Houda, Michal
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We specialize to different approaches when solving an optimization problem where some uncertainties in constraints occur. To overcome uncertainty, we can request the solution to be feasible to all but a small part of constraints. Both approaches gives us different methods to deal with this requirement. We try to find fundamental differencies between them and illustrate the differencies on a simple numerical example.
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Empirické procesy ve stochastickém programování
 Kaňková, Vlasta ; Houda, Michal
Usually, it is very complicated to investigate and to solve optimization problems depending on a probability measure. To this end a stability of them, considers with respect to a prabability measure space, has been discused in the stochastic programming literature many times. The paper is focus on the investigation of the stability with respect to the Wasserstein and to the Komolgorov metrics with "underlying" L_1 space. Moreover, we applay achieved stability results to empirical estimates.
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Odhadování v úlohách s pravděpodobnostními omezeními
 Houda, Michal
Many engineering and economic applications make use of the stochastic programming theory. Major part of models require a complete knowledge of distribution of random parameters, but this assumption is rarely accomplished. We then need to study behaviour of optimal solution when the distribution changes slightly. In our contribution we consider the chance constrained problem;we recapitulated some known theoretical results about stability and estimation of the problem.
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