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The sample average approximation method for stochastic discrete optimization
 SIAM Journal on Optimization
, 2001
"... Abstract. In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and consequently the expected value function is approximated by the corresponding sample average function. The ob ..."
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Cited by 213 (21 self)
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Abstract. In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and consequently the expected value function is approximated by the corresponding sample average function
Testing the Validity of the Averaged Approximation for the IAsys§
"... Abstract—One device used to measure rate constants is the IAsys, and the flow in such a device can be modeled as stagnation point flow. Due to the special nature of the flow, the effects of transport on a surface reaction near a stagnation point may be incorporated exactly as long as the initial con ..."
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concentration of bound state is uniform. However, if the bound state is nonuniform initially, a complicated integrodifferential equation arises for the evolution of the bound state. Such a form is inconvenient for data analysis. The averaged approximation replaces the nonuniform initial state with its average
A Guide to SampleAverage Approximation
, 2011
"... We provide a review of the principle of sampleaverage approximation (SAA) for solving simulationoptimization problems. Our goal is to provide an accessible overview of the area and emphasize interesting recent work. We explain when one might want to use SAA and when one might expect it to provide g ..."
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Cited by 3 (0 self)
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We provide a review of the principle of sampleaverage approximation (SAA) for solving simulationoptimization problems. Our goal is to provide an accessible overview of the area and emphasize interesting recent work. We explain when one might want to use SAA and when one might expect it to provide
Optimal budget allocation for sample average approximation
 Operations Research
, 2013
"... Abstract. The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain estimators of an ..."
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Cited by 2 (0 self)
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Abstract. The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain estimators
1Optimal Budget Allocation for Sample Average Approximation
, 2011
"... Abstract. The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain an estimator of t ..."
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Abstract. The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain an estimator
Stochastic Multiobjective Optimization: Sample Average Approximation and Applications
 J OPTIM THEORY APPL
, 2011
"... We investigate one stage stochastic multiobjective optimization problems where the objectives are the expected values of random functions. Assuming that the closed form of the expected values is difficult to obtain, we apply the well known Sample Average Approximation (SAA) method to solve it. We ..."
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Cited by 1 (0 self)
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We investigate one stage stochastic multiobjective optimization problems where the objectives are the expected values of random functions. Assuming that the closed form of the expected values is difficult to obtain, we apply the well known Sample Average Approximation (SAA) method to solve it. We
Sample average approximation of stochastic dominance constrained programs
 Math. Program
"... In this paper we study optimization problems with secondorder stochastic dominance constraints. This class of problems has been receiving increasing attention in the literature as it allows for the modeling of optimization problems where a riskaverse decision maker wants to ensure that the soluti ..."
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Cited by 7 (3 self)
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apply the Sample Average Approximation (SAA) method to this problem, which results in a semiinfinite program, and study asymptotic convergence of optimal values and optimal solutions, as well as the rate of convergence of the feasibility set of the resulting semiinfinite program as the sample size
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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in a more gen eral setting? We compare the marginals com puted using loopy propagation to the exact ones in four Bayesian network architectures, including two realworld networks: ALARM and QMR. We find that the loopy beliefs of ten converge and when they do, they give a good approximation
Results 1  10
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14,948