#@ s*: Label=FastTest
#@ *: DakotaConfig=HAVE_DOT
# DAKOTA Input File: dakota_ouu1_cantilever.in
# Optimization under uncertainty
# using the cantilever test function.

environment,
	  method_pointer = 'OPTIM'

###########################
# begin opt specification #
###########################
method,
	id_method = 'OPTIM'
	model_pointer = 'OPTIM_M'
	dot_sqp
#	npsol_sqp
	  convergence_tolerance = 1.e-6
	scaling

model,
	id_model = 'OPTIM_M'
	nested
	  variables_pointer  = 'OPTIM_V'
	  sub_method_pointer = 'UQ'
	  responses_pointer  = 'OPTIM_R'
# Approach 1: constrain the probabilities < .00135
#	  primary_response_mapping   = 1. 0. 0. 0. 0. 0. 0. 0. 0.
#	  secondary_response_mapping = 0. 0. 0. 0. 0. 1. 0. 0. 0.
#                                      0. 0. 0. 0. 0. 0. 0. 0. 1.
# Approach 2 (better for sampling): constrain mean + 3 sigma
	  primary_response_mapping   = 1. 0. 0. 0. 0. 0.
	  secondary_response_mapping = 0. 0. 1. 3. 0. 0.
                                       0. 0. 0. 0. 1. 3.

variables,
	id_variables = 'OPTIM_V'
	continuous_design = 2
	  initial_point    2.5    2.5
	  upper_bounds    10.0   10.0
	  lower_bounds     1.0    1.0
	  descriptors 'w' 't'
	scale_type = 'auto'

responses,
# minimize mean Weight
# s.t.     p_fail_S <= .00135 (beta = 3.0) [Approach 1]
#          p_fail_D <= .00135 (beta = 3.0) [Approach 1]
# or
#          mean_S + 3 sigma_S <= 0         [Approach 2]
#          mean_D + 3 sigma_D <= 0         [Approach 2]
#
# NOTE: This specifies the TOTAL RESPONSE for the optimization,
#       which is a combination of nested & interface responses.
	id_responses = 'OPTIM_R'
	objective_functions = 1
	nonlinear_inequality_constraints = 2
# Approach 1 allowables
#	nonlinear_inequality_upper_bounds = .00135 .00135
# Approach 2 uses default upper bounds (mean + 3 sigma < 0.0)
	numerical_gradients
	  method_source dakota
	  interval_type central
	  fd_gradient_step_size = 1.e-2
	no_hessians

##########################
# begin UQ specification #
##########################
method,
	id_method = 'UQ'
	model_pointer = 'UQ_M'
        sampling
	  samples = 50 sample_type lhs
	  seed = 12347 
#	  fixed_seed					#s1
	  output quiet

model,
	id_model = 'UQ_M'
	single
	  variables_pointer = 'UQ_V'
	  interface_pointer = 'UQ_I'
	  responses_pointer = 'UQ_R'

variables,
	id_variables = 'UQ_V'
	continuous_design = 2
	normal_uncertain = 4
	  means             =  40000. 29.E+6 500. 1000.
	  std_deviations    =  2000. 1.45E+6 100. 100.
	  descriptors       =  'R' 'E' 'X' 'Y'

interface,
	id_interface = 'UQ_I'
	direct
	  analysis_driver = 'cantilever'
	  deactivate evaluation_cache restart_file

responses,
	id_responses = 'UQ_R'
	response_functions = 3
	no_gradients
	no_hessians