Program bude průběžně doplňován. Hosté jsou srdečně zváni.
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Reflections on risk and dynamics in stochastic programming models. + upřesnění dalšího programu
- Autor:
- Prof. RNDr. Jitka Dupačová, DrSc.
- Datum:
- 28.2.2008
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Risk-Sensitive Control of Markov Chains and its Application to Portfolio Management.
- Autor:
- Ing. Karel Sladký (ÚTIA)
- Datum:
- 13.3.2008
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Aktuální problémy stochastického programování.
- Autor:
- Mgr. Martin Branda
- Datum:
- 20.3.2008
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Rychlost konvergence - zobecnění.
- Autor:
- RNDr. Vlasta Kaňková
- Datum:
- 27.3.2008
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Stochastická aproximace ve stochastickém programování.
- Autor:
- Prof. RNDr. Václav Dupač, DrSc.
- Datum:
- 10.4.2008
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Disertace.
- Autor:
- RNDr. Jana Čerbáková
- Datum:
- 24.4.2008
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Stochastické programování s náhodným vektorem pravých stran - minimaxový přístup.
- Autor:
- Pavel Kříž
- Datum:
- 15.5.2008
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Adaptive jackknife estimators for stochastic programming.
- Autor:
- Prof. David Morton (USA)
- Datum:
- 22.5.2008
- Abstrakt:
Stochastic programming facilitates decision making under uncertainty. Unfortunately, it is usually impractical to find an optimal solution to a stochastic program. Confidence intervals on the optimal value, or optimal gap of a candidate solution, can be obtained using Monte Carlo approximations. However, the standard point estimate of the optimal value, or optimality gap, contains bias due to the nature of the sampling-based approximation. We provide a method to reduce this bias, and hence provide a better, i.e., tighter, confidence interval on the optimal value or a candidate solution's optimality gap. Our method requires less restrictive assumptions on the structure of the bias than previously-available estimators, and we establish desirable statistical properties of our estimators. Our estimators adapt to problem-specific properties, and we provide a family of estimators, which allows flexibility in choosing the level of aggressiveness for bias reduction. We compare our estimators with known techniques on test problems from the literature.