A fast general extension algorithm of Latin hypercube sampling. Issue 17 (22nd November 2017)

• Record Type: Journal Article
• Authors: Yang, Ming; Liu, Zhizhao; Li, Wei
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 17(2017)
• Page Start: 3398
• Page End: 3411
• Publication Date: 2017-11-22
• Subjects: Latin hypercube sampling -- fast general extension -- time consumption -- block matrix -- upper and lower bounds; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1368080
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: A fast general extension algorithm of Latin hypercube sampling (LHS) is proposed, which reduces the time consumption of basic general extension and preserves the most original sampling points. The extension algorithm starts with an original LHS of size m and constructs a new LHS of size m + n that remains the original points. This algorithm is the further research of basic general extension, which cost too much time to get the new LHS. During selecting the original sampling points to preserve, time consumption is cut from three aspects. The first measure of the proposed algorithm is to select isolated vertices and divide the adjacent matrix into blocks. Secondly, the relationship of original LHS structure and new LHS structure is discussed. Thirdly, the upper and lower bounds help reduce the time consumption. The proposed algorithm is applied for two functions to demonstrate the effectiveness.