#@ s*: Label=FastTest
#@ *: DakotaConfig=HAVE_NPSOL
# DAKOTA INPUT FILE: dakota_trsbouu2_tbch.in
#
# Surrogate-based OUU with trust regions: surrogate containing nested
#

environment,
	method_pointer = 'SBLO'

method,
	id_method = 'SBLO'
	surrogate_based_local
	model_pointer = 'OPTIM_M'
	  approx_method_pointer = 'OPTIM'
	  max_iterations = 50
          soft_convergence_limit = 2
	  trust_region
	    initial_size = 0.4
	    contraction_factor = 0.5
	    expansion_factor   = 1.50

###########################
# begin opt specification #
###########################
method,
	id_method = 'OPTIM'
        npsol_sqp
	  convergence_tolerance = 1.e-8

model,
	id_model = 'OPTIM_M'
	surrogate global
	  variables_pointer = 'OPTIM_V'
	  responses_pointer = 'OPTIM_R'
	  polynomial quadratic
	  dace_method_pointer = 'DACE'

variables,
	id_variables = 'OPTIM_V'
	continuous_design = 2
	  initial_point    1.8    1.0
	  upper_bounds     2.164  4.0
	  lower_bounds     1.5    0.0
	  descriptors      'd1'   'd2'

responses,
# minimize f + p_fail_r1 + p_fail_r2
# s.t.     g_1/2/3 <= 0
#          p_fail_r3 <= 0.1
# 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 = 4
	nonlinear_inequality_upper_bounds = 0.0 0.0 0.0 1.6e+5
	analytic_gradients
#	numerical_gradients
#	  method_source dakota
#	  interval_type central
#	  fd_gradient_step_size = 1.e-5
	no_hessians

############################
# begin DACE specification #
############################
method,
	id_method = 'DACE'
	model_pointer = 'DACE_M'
	sampling
	  sample_type lhs seed = 12347 
#	  fixed_seed					#s1
#	dace lhs seed = 5
#	  samples = 8 symbols = 8

model,
	id_model = 'DACE_M'
	nested
	  variables_pointer  = 'OPTIM_V'
	  sub_method_pointer = 'UQ'
	  optional_interface_pointer  = 'OPTIONAL_I'
	  optional_interface_responses_pointer = 'OPTIONAL_I_R'
	  responses_pointer  = 'OPTIM_R_NO_GRAD'
	  primary_response_mapping   = 0. 0. 1. 0. 0. 0. 0. 0. 1.
	  secondary_response_mapping = 0. 0. 0. 1. 3. 0. 0. 0. 0.

responses,
	id_responses = 'OPTIM_R_NO_GRAD'
	objective_functions = 1
	nonlinear_inequality_constraints = 4
	no_gradients
	no_hessians

##########################################
# begin optional interface specification #
##########################################
interface,
	id_interface = 'OPTIONAL_I'
	direct
	  analysis_driver = 'cyl_head'

responses,
# NOTE: This is the response set from the optional interface.
	id_responses = 'OPTIONAL_I_R'
	objective_functions = 1
	nonlinear_inequality_constraints = 3
	no_gradients
	no_hessians

##########################
# begin UQ specification #
##########################
method,
	id_method = 'UQ'
	model_pointer = 'UQ_M'
        sampling
	  sample_type lhs samples = 50
	  seed = 12347 
#	  fixed_seed					#s1
	  response_levels = 4.2e+11 1.6e+05 7.5e+05
	  complementary distribution
	  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 = 2
	  means             =  248.89, 593.33
	  std_deviations    =   12.4,  29.7
	uniform_uncertain = 2
	  lower_bounds =  199.3,  474.63
	  upper_bounds =  298.5,  712.
	weibull_uncertain = 2
	  alphas            =    12., 30.
	  betas             =   250., 590.

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

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