Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

(Deprecated keyword) Augments an existing Latin Hypercube Sampling (LHS) study
Alias: none
Argument(s): none
Default: no sample reuse in coefficient estimation
This keyword is deprecated. Instead specify sample_type
lhs
with refinement_samples
.
An incremental random sampling approach will augment an existing random sampling study with refinement_samples to get better estimates of mean, variance, and percentiles. The number of refinement_samples in each refinement level must result in twice the number of previous samples.
Typically, this approach is used when you have an initial study with sample size N1 and you want to perform an additional N1 samples. Ideally, a Dakota restart file containing the initial N1 samples, so only N1 (instead of 2 x N1) potentially expensive function evaluations will be performed.
This process can be extended by repeatedly doubling the refinement_samples:
method sampling samples = 50 refinement_samples = 50 100 200 400 800
Usage Tips
The incremental approach is useful if it is uncertain how many simulations can be completed within available time.
See the examples below and the Usage and Restarting Dakota Studies pages.
Suppose an initial study is conducted using sample_type
random
with samples
= 50. A followon study uses a new input file where samples
= 50, and refinement_samples
= 50, resulting in 50 reused samples (from restart) and 50 new random samples. The 50 new samples will be combined with the 50 previous samples to generate a combined sample of size 100 for the analysis.
One way to ensure the restart file is saved is to specify a nondefault name, via a command line option:
dakota input LHS_50.in write_restart LHS_50.rst
which uses the input file:
# LHS_50.in environment tabular_data tabular_data_file = 'LHS_50.dat' method sampling sample_type lhs samples = 50 model single variables uniform_uncertain = 2 descriptors = 'input1' 'input2' lower_bounds = 2.0 2.0 upper_bounds = 2.0 2.0 interface analysis_drivers 'text_book' fork responses response_functions = 1 no_gradients no_hessians
and the restart file is written to LHS_50.rst
.
Then an incremental LHS study can be run with:
dakota input LHS_100.in read_restart LHS_50.rst write_restart LHS_100.rst
where LHS_100.in
is shown below, and LHS_50.rst
is the restart file containing the results of the previous LHS study.
# LHS_100.in environment tabular_data tabular_data_file = 'LHS_100.dat' method sampling sample_type incremental_lhs samples = 50 refinement_samples = 50 model single variables uniform_uncertain = 2 descriptors = 'input1' 'input2' lower_bounds = 2.0 2.0 upper_bounds = 2.0 2.0 interface analysis_drivers 'text_book' fork responses response_functions = 1 no_gradients no_hessians
The user will get 50 new LHS samples which maintain both the correlation and stratification of the original LHS sample. The new samples will be combined with the original samples to generate a combined sample of size 100.