Displaying similar documents to “Sequential importance sampling algorithms for dynamic stochastic programming.”

Reduced basis solver for stochastic Galerkin formulation of Darcy flow with uncertain material parameters

Béreš, Michal

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In this contribution, we present a solution to the stochastic Galerkin (SG) matrix equations coming from the Darcy flow problem with uncertain material coefficients in the separable form. The SG system of equations is kept in the compressed tensor form and its solution is a very challenging task. Here, we present the reduced basis (RB) method as a solver which looks for a low-rank representation of the solution. The construction of the RB consists of iterative expanding of the basis...

Image sampling with quasicrystals.

Grundland, Mark, Patera, Jirí, Masáková, Zuzana, Dodgson, Neil A. (2009)

SIGMA. Symmetry, Integrability and Geometry: Methods and Applications [electronic only]

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Fixed precision optimal allocation in two-stage sampling

Wojciech Niemiro, Jacek Wesołowski (2001)

Applicationes Mathematicae

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Two-stage sampling schemes arise in survey sampling, especially in situations when the complete update of the frame is difficult. In this paper we solve the problem of fixed precision optimal allocation in two special two-stage sampling schemes. The solution is based on reducing the original question to an eigenvalue problem and then using the Perron-Frobenius theorem.

Stochastic dynamic programming with random disturbances

Regina Hildenbrandt (2003)

Discussiones Mathematicae Probability and Statistics

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Several peculiarities of stochastic dynamic programming problems where random vectors are observed before the decision ismade at each stage are discussed in the first part of this paper. Surrogate problems are given for such problems with distance properties (for instance, transportation problems) in the second part.