An improved simulation budget allocation procedure to efficiently select the optimal subset of many alternatives

Zhang, Si; Lee, Loo Hay; Chew, Ek Peng; Chen, Chun‐Hung; Jen, Henyi

doi:10.1109/coase.2012.6386330

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…The sampling procedures in the Bayesian branch aim to either maximize the PCS or minimize the expected opportunity cost subject to a given sampling budget (see, e.g., [15], [16], [17]). Chen et al [18], Zhang et al [19], and Gao and Chen [20] study sampling procedures to maximize the PCS for selecting an optimal subset; Xiao and Lee [21] derive the convergence rate of the false subset-selection probability, and offer an allocation rule achieving an asymptotically optimal convergence rate; and Gao and Chen [22] develop a sampling procedure based on the expected opportunity cost. In R&S, the alternatives are ranked by the expectations of their sample performance, which can be directly estimated by the sample average of each alternative, whereas in our problem, the nodes are ranked by the stationary probabilities of the Markov chain, which are estimated indirectly from the interaction samples between different nodes.…”

Section: Introductionmentioning

confidence: 99%

Efficient Sampling for Selecting Important Nodes in Random Network

Li,

Xu,

Peng

et al. 2019

Preprint

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We consider the problem of selecting important nodes in a random network, where the nodes connect to each other randomly with certain transition probabilities. The node importance is characterized by the stationary probabilities of the corresponding nodes in a Markov chain defined over the network, as in Google's PageRank. Unlike deterministic network, the transition probabilities in random network are unknown but can be estimated by sampling. Under a Bayesian learning framework, we apply the first-order Taylor expansion and normal approximation to provide a computationally efficient posterior approximation of the stationary probabilities. In order to maximize the probability of correct selection, we propose a dynamic sampling procedure which uses not only posterior means and variances of certain interaction parameters between different nodes, but also the sensitivities of the stationary probabilities with respect to each interaction parameter. Numerical experiment results demonstrate the superiority of the proposed sampling procedure.

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Section: Introductionmentioning

confidence: 99%

Efficient Sampling for Selecting Important Nodes in Random Network

Li,

Xu,

Peng

et al. 2019

Preprint

Self Cite

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“…The three popular procedures of finding the best design are compared in [10]. Extensions of the research in OCBA and ranking and selection include subset selection [11][12][13], selecting the Pareto set for multiple objective functions [14], selecting the best design subject to stochastic constraints [15,16], and complete ranking [17]. A detailed summary of the existing work in OCBA can be found in [18].…”

Section: Introductionmentioning

confidence: 99%

Efficient Simulation Budget Allocation for Ranking the TopmDesigns

Xiao

Lee

2014

Discrete Dynamics in Nature and Society

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We consider the problem of ranking the topmdesigns out ofkalternatives. Using the optimal computing budget allocation framework, we formulate this problem as that of maximizing the probability of correctly ranking the topmdesigns subject to the constraint of a fixed limited simulation budget. We derive the convergence rate of the false ranking probability based on the large deviation theory. The asymptotically optimal allocation rule is obtained by maximizing this convergence rate function. To implement the simulation budget allocation rule, we suggest a heuristic sequential algorithm. Numerical experiments are conducted to compare the effectiveness of the proposed simulation budget allocation rule. The numerical results indicate that the proposed asymptotically optimal allocation rule performs the best comparing with other allocation rules.

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“…The procedure, called OCBA-m, allocates a limited computational budget across the k systems to maximize the probability of correct selection (PCS) that the top-m systems are chosen. Zhang et al (2012) present an improved version of this algorithm, called OCBA-m+, while LaPorte et al (2012) extend OCBA for subset selection under very small computing budgets. Ryzhov and Powell (2009) have also recently developed a subset selection algorithm for online problems.…”

Section: Introductionmentioning

confidence: 99%

A procedure to select the best subset among simulated systems using economic opportunity cost

Chingcuanco¹,

Osorio²

2013

2013 Winter Simulations Conference (WSC)

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We consider subset selection problems in ranking and selection with tight computational budgets. We develop a new procedure that selects the best m out of k stochastic systems. Previous approaches have focused on individually separating out the top m from all the systems being considered. We reformulate the problem by casting all m-sized subsets of the k systems as the alternatives of the selection problem. This reformulation enables our derivation to follow along traditional ranking and selection frameworks. In particular, we extend the value of information procedure to subset selection. Furthermore, unlike previous subset selection efforts, we use an expected opportunity cost (EOC) loss function as evidence for correct selection. In minimizing the EOC, we consider both deriving an asymptotic allocation rule as well as approximately solving the underlying optimization problem. Experiments show the advantage of our approach for tests with small computational budgets.

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An improved simulation budget allocation procedure to efficiently select the optimal subset of many alternatives

Cited by 11 publications

References 18 publications

Efficient Sampling for Selecting Important Nodes in Random Network

Efficient Sampling for Selecting Important Nodes in Random Network

Efficient Simulation Budget Allocation for Ranking the TopmDesigns

A procedure to select the best subset among simulated systems using economic opportunity cost

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