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  • In stochastic optimization problems, expectation of random function is often being minimized. Since the expectation can rarely be evaluated exactly an approximation has to be done. In the present paper, three types of approximation are dealt with: discretization, Monte Carlo and Quasi Monte Carlo. Convergence rate of the approximation error is evaluated and some upper bounds of the error are given.
  • In stochastic optimization problems, expectation of random function is often being minimized. Since the expectation can rarely be evaluated exactly an approximation has to be done. In the present paper, three types of approximation are dealt with: discretization, Monte Carlo and Quasi Monte Carlo. Convergence rate of the approximation error is evaluated and some upper bounds of the error are given. (en)
Title
  • Notes on approximation of stochastic programming problem.
  • Notes on approximation of stochastic programming problem. (en)
skos:prefLabel
  • Notes on approximation of stochastic programming problem.
  • Notes on approximation of stochastic programming problem. (en)
skos:notation
  • RIV/67985556:_____/03:16030220!RIV/2004/AV0/A16004/N
http://linked.open.../vavai/riv/strany
  • 244;251
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  • P(GA402/01/0539), Z(AV0Z1075907)
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  • 618232
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  • RIV/67985556:_____/03:16030220
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  • stochastic programming; discretization; Monte Carlo (en)
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  • [1F44A7974EE1]
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  • Prague [CZ]
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  • Prague
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  • Proceedings of the 21st International Conference Mathematical Methods in Economics 2003.
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  • Šmíd, Martin
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number of pages
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  • Česká zemědělská univerzita v Praze
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  • 80-213-1046-4
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