Dakota Reference Manual  Version 6.12
Explore and Predict with Confidence
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Sample Input Files

A Dakota input file is a collection of fields from the dakota.input.summary file that describe the problem to be solved by Dakota. Several examples follow.

Sample 1: Optimization

The following sample input file shows single-method optimization of the Textbook Example (see Textbook) using DOT's modified method of feasible directions. A similar file is available as dakota/share/dakota/examples/users/textbook_opt_conmin.in.

# Dakota Input File: textbook_opt_conmin.in
environment
 tabular_data
  tabular_data_file = 'textbook_opt_conmin.dat'

method
# dot_mmfd #DOT performs better but may not be available
 conmin_mfd
  max_iterations = 50
  convergence_tolerance = 1e-4

variables
 continuous_design = 2
  initial_point  0.9  1.1
  upper_bounds   5.8  2.9
  lower_bounds   0.5  -2.9
  descriptors   'x1'  'x2'

interface
 direct
  analysis_driver =    'text_book'

responses
 objective_functions = 1
 nonlinear_inequality_constraints = 2
 numerical_gradients
  method_source dakota
  interval_type central
  fd_gradient_step_size = 1.e-4
 no_hessians

Sample 2: Least Squares (Calibration)

The following sample input file shows a nonlinear least squares (calibration) solution of the Rosenbrock Example (see Rosenbrock) using the NL2SOL method. A similar file is available as dakota/share/dakota/examples/users/rosen_opt_nls.in

# Dakota Input File: rosen_opt_nls.in
environment
 tabular_data
  tabular_data_file = 'rosen_opt_nls.dat'

method
 max_iterations = 100
 convergence_tolerance = 1e-4
 nl2sol

model
 single

variables
 continuous_design = 2
  initial_point  -1.2   1.0
  lower_bounds   -2.0   -2.0
  upper_bounds   2.0   2.0
  descriptors    'x1'   "x2"

interface
 analysis_driver = 'rosenbrock'
  direct

responses
 calibration_terms = 2
 analytic_gradients
 no_hessians

Sample 3: Nondeterministic Analysis

The following sample input file shows Latin Hypercube Monte Carlo sampling using the Textbook Example (see Textbook). A similar file is available as dakota/share/dakota/test/dakota_uq_textbook_lhs.in.

method,
    sampling,
     samples = 100 seed = 1
     complementary distribution
     response_levels = 3.6e+11 4.0e+11 4.4e+11
              6.0e+04 6.5e+04 7.0e+04
              3.5e+05 4.0e+05 4.5e+05
     sample_type lhs

variables,
    normal_uncertain = 2
     means       = 248.89, 593.33
     std_deviations  =  12.4,  29.7
     descriptors    = 'TF1n' 'TF2n'
    uniform_uncertain = 2
     lower_bounds   = 199.3, 474.63
     upper_bounds   = 298.5, 712.
     descriptors    = 'TF1u' 'TF2u'
    weibull_uncertain = 2
     alphas      =  12.,  30.
     betas       = 250.,  590.
     descriptors    = 'TF1w' 'TF2w'
    histogram_bin_uncertain = 2
     num_pairs  = 3     4
     abscissas  = 5 8 10 .1 .2 .3 .4
     counts   = 17 21 0 12 24 12  0
     descriptors = 'TF1h' 'TF2h'
    histogram_point_uncertain = 1
     num_pairs  = 2
     abscissas  = 3 4
     counts   = 1 1
     descriptors = 'TF3h'

interface,
    fork asynch evaluation_concurrency = 5
     analysis_driver = 'text_book'

responses,
    response_functions = 3
    no_gradients
    no_hessians

Sample 4: Parameter Study

The following sample input file shows a 1-D vector parameter study using the Textbook Example (see Textbook). It makes use of the default environment and model specifications, so they can be omitted. A similar file is available in the test directory as dakota/share/dakota/examples/users/rosen_ps_vector.in.

# Dakota Input File: rosen_ps_vector.in
environment
 tabular_data
  tabular_data_file = 'rosen_ps_vector.dat'

method
 vector_parameter_study
  final_point = 1.1 1.3
  num_steps = 10

variables
 continuous_design = 2
  initial_point  -0.3   0.2
  descriptors    'x1'   "x2"

interface
 analysis_driver = 'rosenbrock'
  direct

responses
 objective_functions = 1
 no_gradients
 no_hessians

Sample 5: Hybrid Strategy

The following sample input file shows a hybrid environment using three methods. It employs a genetic algorithm, pattern search, and full Newton gradient-based optimization in succession to solve the Textbook Example (see Textbook). A similar file is available as dakota/share/dakota/examples/users/textbook_hybrid_strat.in.

environment
 hybrid sequential
  method_list = 'PS' 'PS2' 'NLP'

method
 id_method = 'PS'
 model_pointer = 'M1'
 coliny_pattern_search stochastic
  seed = 1234
  initial_delta = 0.1
  variable_tolerance = 1.e-4
  solution_accuracy = 1.e-10
  exploratory_moves basic_pattern
  #verbose output

method
 id_method = 'PS2'
 model_pointer = 'M1'
 max_function_evaluations = 10
 coliny_pattern_search stochastic
  seed = 1234
  initial_delta = 0.1
  variable_tolerance = 1.e-4
  solution_accuracy = 1.e-10
  exploratory_moves basic_pattern
  #verbose output

method
 id_method = 'NLP'
 model_pointer = 'M2'
    optpp_newton
  gradient_tolerance = 1.e-12
  convergence_tolerance = 1.e-15
  #verbose output

model
 id_model = 'M1'
 single
  variables_pointer = 'V1'
  interface_pointer = 'I1'
  responses_pointer = 'R1'

model
 id_model = 'M2'
 single
  variables_pointer = 'V1'
  interface_pointer = 'I1'
  responses_pointer = 'R2'

variables
 id_variables = 'V1'
 continuous_design = 2
  initial_point  0.6  0.7
  upper_bounds   5.8  2.9
  lower_bounds   0.5  -2.9
  descriptors   'x1'  'x2'

interface
 id_interface = 'I1'
 direct
  analysis_driver= 'text_book'

responses
 id_responses = 'R1'
 objective_functions = 1
 no_gradients
 no_hessians

responses
 id_responses = 'R2'
 objective_functions = 1
 analytic_gradients
 analytic_hessians

Additional example input files, as well as the corresponding output, are provided in the Tutorial chapter of the Users Manual [5].