Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach
نویسندگان
چکیده
منابع مشابه
Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach
Stochastic programming models are large-scale optimization problems that are used to facilitate decisionmaking under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it requires the evaluation of a multidimensional integ...
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Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it involves the evaluation of a multidimensional inte...
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ژورنال
عنوان ژورنال: INFORMS Journal on Computing
سال: 2015
ISSN: 1091-9856,1526-5528
DOI: 10.1287/ijoc.2014.0630