Covariance Matrix Options
These options are provided for calculating covariance matrix during the second
and third data reduction stages. Figure 1 shows the covariance matrix options.
Each option is explained below:
- Do You Want To Stack Datasets? - Options are 'Yes' and 'No'.
- Yes - Data sets are stacked to compute covariance matrix. This option
assumes that there is enough RAM available to stack the data sets. Please note
that full storage of covariance matrix is required when you select this option.
- No - A pair of data sets are loaded at a time to compute covariance matrix.
This option uses less memory usage but it requires N*(N-1)/2 loops to compute
the covariance matrix where N is the number of data sets.
- 'Select Matrix Storage Type' - Options are 'Full' and 'Packed'. You have
the option to store only lower triangular portion of the symmetric matrix with
the packed storage scheme.
- 'Select Precision' - Options are 'Double' and 'Single'. Single precision
uses 50% less memory required when compared to double precision. Single
precision is accurate up to 7 digits after decimal point.
- 'Select Eigen Solver Type' - Options are 'Selective' and 'All'. These
options will be used only for the packed storage scheme.
- 'Selective' - Only a few desired eigen values are computed. This option
will compute eigen values faster when compared to 'All' option. However, if
there are convergence issues use option 'All' to compute eigen values.
- 'All' - All eigen values are computed. We recommend to use this option for
computing eigen values only when the selective eigen solver doesn't converge.
Note: If you want a better performance during the data
reduction stage, use full storage of covariance matrix and single precision.
However, if you want less memory usage during the data reduction stage use
packed storage scheme, single precision and selective eigen solver.
Figure 1: Covariance Matrix Options