Table 1 The algorithm of OCMA calculates the right outcome. Eigenvectors (or singular vectors) are compared using Infinite norm Embedded Image, Taxicab norm Embedded Image, and Euclidean norm Embedded Image. We use Embedded Image and Embedded Image to denote the vectors calculated by OCMA and Embedded Image and Embedded Image to denote the vectors calculated by MATLAB (version 2012b). The comparison for eigen-decomposition is presented in the upper table, and the comparison for singular-value decomposition is presented in the lower one. In the upper table, N denotes the number of individuals. In lower table, N = 1,000,000, and M denotes the number of genetic markers. The configuration of the personal computer: Intel Core i7-6700 CPU (4 cores), Memory = 24GB. Disk = Samsung SSD 850 EVO 250GB. The operating system is Windows 7. Time is measured by wall-clock (instead of CPU time)
NEmbedded ImageComputation time (s)
Embedded ImageEmbedded ImageEmbedded ImageOCMAMATLAB
10005.2*10−77.6*10−81.1*10−70.10.4
20002.8*10−78.1*10−81.0*10−70.52.8
50004.3*10−72.1*10−72.4*10−76.435.5
100001.6*10−65.7*10−76.3*10−746.2294.0
200003.3*10−64.4*10−64.3*10−6246.42905.1
MEmbedded ImageComputation time (s)
Embedded ImageEmbedded ImageEmbedded ImageOCMAMATLAB
1002.9*10−51.2*10−48.9*10−510.212.3
2002.1*10−59.2*10−56.6*10−514.829.6
5001.9*10−57.7*10−55.6*10−547.590.4
10001.6*10−51.3*10−48.5*10−589.5206.4
20001.2*10−51.1*10−47.1*10−5226.1726.9