Optimal Sliced Latin Hypercube Designs

2014/09/05 by Shan Ba, William R. Myers, William A. Brenneman · 163 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Algorithm #Categorical variable #Column (typography) #Computer science #Hypercube #Latin hypercube sampling #Mathematical optimization #Mathematics #Monte Carlo method #Optimal Experimental Design Methods #Orthogonal array #Parallel computing #Permutation (music) #Space (punctuation) #Taguchi methods #VLSI and FPGA Design Techniques

paper · doi:10.1080/00401706.2014.957867

published in Technometrics 57(4), 479-487 (Taylor & Francis)

openalex publication_date 2014/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

Sliced Latin hypercube designs (SLHDs) have important applications in designing computer experiments with continuous and categorical factors. However, a randomly generated SLHD can be poor in terms of space-filling, and based on the existing construction method that generates the SLHD column by column using sliced permutation matrices, it is also difficult to search for the optimal SLHD. In this article, we develop a new construction approach that first generates the small Latin hypercube design in each slice and then arranges them together to form the SLHD. The new approach is intuitive and can be easily adapted to generate orthogonal SLHDs and orthogonal array-based SLHDs. More importantly, it enables us to develop general algorithms that can search for the optimal SLHD efficiently.

Citations

• A generalized discrepancy and quadrature error bound
• Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code

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