L
L. Jeff Hong
Researcher at Fudan University
Publications - 97
Citations - 2659
L. Jeff Hong is an academic researcher from Fudan University. The author has contributed to research in topics: Estimator & Selection (genetic algorithm). The author has an hindex of 24, co-authored 91 publications receiving 2166 citations. Previous affiliations of L. Jeff Hong include Hong Kong University of Science and Technology & City University of Hong Kong.
Papers
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Journal ArticleDOI
Discrete Optimization via Simulation Using COMPASS
L. Jeff Hong,Barry L. Nelson +1 more
TL;DR: In this article, an optimization-via-simulation algorithm, called COMPASS, was proposed for estimating the performance measure via a stochastic, discrete-event simulation, and the decision variables were integer ordered.
Journal ArticleDOI
Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach
L. Jeff Hong,Yi Yang,Liwei Zhang +2 more
TL;DR: It is shown that the solutions of the sequence of approximations converge to a Karush-Kuhn-Tucker (KKT) point of the JCCP under a certain asymptotic regime.
Journal ArticleDOI
Estimating Quantile Sensitivities
TL;DR: This paper proposes an infinitesimal-perturbation-analysis (IPA) estimator and shows that the quantile sensitivities can be written in the form of conditional expectations, and obtains a consistent estimator by dividing data into batches and averaging the IPA estimates of all batches.
Journal ArticleDOI
Simulating Sensitivities of Conditional Value at Risk
L. Jeff Hong,Guangwu Liu +1 more
TL;DR: This paper proves that the CVaR sensitivity can be written as a conditional expectation for general loss distributions, and proposes and demonstrates how to use the estimator to solve optimization problems withCVaR objective and/or constraints, and compares it to a popular linear programming-based algorithm.
Proceedings ArticleDOI
A brief introduction to optimization via simulation
L. Jeff Hong,Barry L. Nelson +1 more
TL;DR: Three types of OvS problems are introduced: the R&S problems, the continuous OvSblems and the discrete OvS Problems, and issues and current research development for these problems are discussed.