1Blurb:: 2(Deprecated keyword) Augments an existing Latin Hypercube Sampling (LHS) study 3 4Description:: 5This keyword is deprecated. Instead specify \c sample_type \c lhs 6with \c refinement_samples. 7 8An incremental random sampling approach will augment an existing 9random sampling study with refinement_samples to get better estimates 10of mean, variance, and percentiles. The number of refinement_samples 11in each refinement level must result in twice the number of previous 12samples. 13 14Typically, this approach is used when you have an initial study with 15sample size N1 and you want to perform an additional N1 samples. 16Ideally, a Dakota restart file containing the initial N1 samples, so 17only N1 (instead of 2 x N1) potentially expensive function 18evaluations will be performed. 19 20This process can be extended by repeatedly doubling the \c refinement_samples: 21\verbatim 22method 23 sampling 24 seed = 1337 25 samples = 50 26 refinement_samples = 50 100 200 400 800 27\endverbatim 28 29<b> Usage Tips </b> 30 31The incremental approach is useful if it is uncertain how many 32simulations can be completed within available time. 33 34See the examples below and 35the \ref running_dakota-usage and \ref dakota_restart pages. 36 37Topics:: 38Examples:: 39 40Suppose an initial study is conducted using \c sample_type \c lhs 41with \c samples = 50. A follow-on study uses a new input file where 42\c samples = 50, and \c refinement_samples = 50, resulting in 50 43reused samples (from restart) and 50 new random samples. The 50 new 44samples will be combined with the 50 previous samples to generate a 45combined sample of size 100 for the analysis. 46 47One way to ensure the restart file is saved is to specify a non-default name, 48via a command line option: 49\verbatim 50dakota -input LHS_50.in -write_restart LHS_50.rst 51\endverbatim 52 53which uses the input file: 54 55\verbatim 56# LHS_50.in 57 58environment 59 tabular_data 60 tabular_data_file = 'LHS_50.dat' 61 62method 63 sampling 64 seed = 1337 65 sample_type lhs 66 samples = 50 67 68model 69 single 70 71variables 72 uniform_uncertain = 2 73 descriptors = 'input1' 'input2' 74 lower_bounds = -2.0 -2.0 75 upper_bounds = 2.0 2.0 76 77interface 78 analysis_drivers 'text_book' 79 fork 80 81responses 82 response_functions = 1 83 no_gradients 84 no_hessians 85\endverbatim 86and the restart file is written to \c LHS_50.rst. 87 88 89Then an incremental LHS study can be run with: 90\verbatim 91dakota -input LHS_100.in -read_restart LHS_50.rst -write_restart LHS_100.rst 92\endverbatim 93where \c LHS_100.in is shown below, and \c LHS_50.rst is the restart 94file containing the results of the previous LHS study. In the example input 95files for the initial and incremental studies, the values for \c seed match. 96This ensures that the initial 50 samples generated in both runs are the same. 97\verbatim 98# LHS_100.in 99 100environment 101 tabular_data 102 tabular_data_file = 'LHS_100.dat' 103 104method 105 sampling 106 seed = 1337 107 sample_type incremental_lhs 108 samples = 50 109 refinement_samples = 50 110 111model 112 single 113 114variables 115 uniform_uncertain = 2 116 descriptors = 'input1' 'input2' 117 lower_bounds = -2.0 -2.0 118 upper_bounds = 2.0 2.0 119 120interface 121 analysis_drivers 'text_book' 122 fork 123 124responses 125 response_functions = 1 126 no_gradients 127 no_hessians 128\endverbatim 129 130The user will get 50 new LHS samples which 131maintain both the correlation and stratification of the original LHS 132sample. The new samples will be combined with the original 133samples to generate a combined sample of size 100. 134 135Theory:: 136Faq:: 137See_Also:: 138