Dakota Reference Manual: Sample Input Files

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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 brief examples follow below.

Dakota 6.14 and newer include a refreshed Examples Library, ranging from examples of Dakota input files and basic studies through complete case studies. These can be found in binary distributions in `dakota/share/dakota/examples/official` (`dakota/dakota-examples` in source distributions). Legacy examples, including many from the Dakota software manuals appear in other directories in `dakota/share/dakota/examples` (`dakota/examples` for source).

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 [4].