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DAGSTUHL
2007
14 years 11 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys
CVPR
2010
IEEE
13 years 7 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu
CDC
2008
IEEE
148views Control Systems» more  CDC 2008»
15 years 4 months ago
Convergence rate for stochastic consensus algorithms with time-varying noise statistics: Asymptotic normality
— This paper studies consensus seeking over noisy networks with time-varying noise statistics. Stochastic approximation type algorithms can ensure consensus in mean square and wi...
Minyi Huang
TASE
2008
IEEE
14 years 9 months ago
Stochastic Modeling of an Automated Guided Vehicle System With One Vehicle and a Closed-Loop Path
Abstract--The use of automated guided vehicles (AGVs) in material-handling processes of manufacturing facilities and warehouses isbecomingincreasinglycommon.AcriticaldrawbackofanAG...
Aykut F. Kahraman, Abhijit Gosavi, Karla J. Oty
APPROX
2010
Springer
188views Algorithms» more  APPROX 2010»
14 years 11 months ago
Approximation Algorithms for Reliable Stochastic Combinatorial Optimization
We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wT x over an arbitrary combinatorial feasible set F {0, 1}n . For these pro...
Evdokia Nikolova