Algorithmic construction of optimal symmetric Latin hypercube designs

By Kenny Q. Ye, William Li, Agus Sudjianto. 2000

449 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/01147bbec4d6223a2e85854f089b6b0d48b0cbfe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “These are orthogonal arrays [17-19], various Latin Hypercube designs [20-24], Hammersley sequences [25, 26], and uniform designs [27].”
THE COYOTE UNIVERSE. II. COSMOLOGICAL MODELS AND PRECISION EMULATION OF THE NONLINEAR MATTER POWER SPECTRUM (2009, methodology): “No exchange is needed if element i is exchanged with element n + 1 − i (for an excellent description of the procedure see Ye et al. 2000).”
Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition (2009, methodology): “Different permutation methods have been proposed and adopted in LHS to ensure the correlations between the rows of matrix to be a minimum, for example, correlation reduction with genetic algorithm [21], columnwise-pairwise algorithm [22], single-switch optimization [23], Cholesky decomposition [24],”
Assessing the impacts of parameter uncertainty for computationally expensive groundwater models (2006, methodology): “A symmetric Latin hypercube design (SLHD) [Ye et al., 2000] is used to generate these J experimental design points where J is much less thanN.”
An Efficient Algorithm for Constructing Optimal Design of Computer Experiments (2005, methodology): “For example, Ye, et al (2000) reported that generating an optimal 25×4 LHDs using CP could take several hours on a Sun SPARC 20 workstation.”
The generalization of Latin hypercube sampling (2015, background): “The widespread popularity of LHS has led to the invention of numerous variants meant to improve space-filling [5, 6, 7, 8, 9, 10, 11], optimize projective properties [12], minimize least square error and maximize entropy [13], and reduce spurious correlations [14, 15, 16, 17, 7, 9, 18].”
ORTHOGONAL-MAXIMIN LATIN HYPERCUBE DESIGNS (2008, methodology): “A perturbation X try of a design X is generated by interchanging two randomly chosen elements within a randomly chosen column in X .”
Surrogate-based Analysis and Optimization (2005, background): “The resulting designs have been shown to be relatively insensitive to the optimal design criteria [39].”
Surrogate-based methods for black-box optimization (2017, methodology): “In [57], an columnwise-pairwise algorithm is proposed for ﬁnding optimal designs within this class.”
An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models (2015, methodology): “, 1995), and a columnwise-pairwise exchange algorithm (Ye et al., 2000).”
Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine 1 (2009, background): “The SLHDs have shown better performance than simple LH (Ye et al. , 2000).”
Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points (2003, methodology): “The author extends special thanks to Prof. R. Haftka at the University of Florida for his early comments on D-optimal designs and Latin Hypercube Designs.”
A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions (2007, unknown): “hypercube design (SLHD) (Ye et al. 2000) with n0 = 2 d+ 1 points.”