#@ *: DakotaConfig=HAVE_NPSOL
# s8: test correct traits coverage


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 = 1.0
	    contraction_factor = 0.5
	    expansion_factor   = 1.50
	  output verbose

###########################
# begin opt specification #
###########################
method,
	id_method = 'OPTIM'
	npsol_sqp
#	dot_sqp
	  convergence_tolerance = 1.e-6
	  output verbose

model,
	id_model = 'OPTIM_M'
	variables_pointer  = 'OPTIM_V'
	responses_pointer  = 'OPTIM_R'
	surrogate hierarchical
	  ordered_model_fidelities = 'LO_FI' 'HI_FI'
	  correction additive first_order	#s0,#s2,#s4,#s6
#	  correction additive second_order	#s1,#s3,#s5,#s7,#s8

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'
#   linear_equality_constraint_matrix = 2. 1.       #s8
#   linear_equality_targets = 4.0                   #s8
#   linear_inequality_constraint_matrix = 1. 1.     #s8
#   linear_inequality_upper_bounds = 5.0            #s8

responses,
# 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				#s0,#s1,#s2,#s3,#s4,#s5,#s6,#s7
#	nonlinear_equality_constraints = 1					#s8
#	nonlinear_inequality_constraints = 1				#s8
	nonlinear_inequality_lower_bounds = 3. 3.			#s0,#s1,#s2,#s3,#s4,#s5,#s6,#s7
	nonlinear_inequality_upper_bounds = 1.e+50 1.e+50	#s0,#s1,#s2,#s3,#s4,#s5,#s6,#s7
	analytic_gradients
	no_hessians				#s0,#s2,#s4,#s6
#	quasi_hessians sr1			#s1,#s3,#s5,#s7,#s8

responses,
# NOTE: This specifies the TOTAL RESPONSE for the optimization,
#       which is a combination of nested & interface responses.
	id_responses = 'OPTIM_R_FD'
	objective_functions = 1
	nonlinear_inequality_constraints = 2				#s0,#s1,#s2,#s3,#s4,#s5,#s6,#s7
#	nonlinear_equality_constraints = 1					#s8
#	nonlinear_inequality_constraints = 1				#s8
	nonlinear_inequality_lower_bounds = 3. 3.			#s0,#s1,#s2,#s3,#s4,#s5,#s6,#s7
	nonlinear_inequality_upper_bounds = 1.e+50 1.e+50	#s0,#s1,#s2,#s3,#s4,#s5,#s6,#s7
	numerical_gradients
#	mixed_gradients
#	  id_analytic_gradients  = 1
#	  id_numerical_gradients = 2 3
	no_hessians				#s0,#s2,#s4,#s6
#	quasi_hessians sr1			#s1,#s3,#s5,#s7,#s8

##########################
# begin LF specification #
##########################
model,
	id_model = 'LO_FI'
	nested
	  variables_pointer  = 'OPTIM_V'
	  sub_method_pointer = 'UQ_COMBINED_EXP'
	  responses_pointer  = 'OPTIM_R'	#s0,#s1,#s4,#s5
#	  responses_pointer  = 'OPTIM_R_FD'	#s2,#s3,#s6,#s7,#s8
# maximize beta
	  primary_response_mapping   = 1. 0. 0. 0. 0. 0. 0. 0.
	  secondary_response_mapping = 0. 0. 0. 0. 1. 0. 0. 0.
				       0. 0. 0. 0. 0. 0. 0. 1.

method,
	id_method = 'UQ_COMBINED_EXP'
	model_pointer = 'UQ_M_LO_FI'
	stoch_collocation			#s0,#s1,#s4,#s5
	  quadrature_order = 3			#s0,#s1
#	  sparse_grid_level = 4 non_nested	#s4,#s5
#	local_reliability			#s2,#s3,#s6,#s7,#s8
	  num_response_levels = 0 1 1
	  response_levels = 0.0 0.0
	  compute reliabilities
	  complementary distribution

##########################
# begin HF specification #
##########################
model,
	id_model = 'HI_FI'
	nested
	  variables_pointer  = 'OPTIM_V'
	  sub_method_pointer = 'UQ_UNCERTAIN_EXP'
	  responses_pointer  = 'OPTIM_R'
# maximize beta
	  primary_response_mapping   = 1. 0. 0. 0. 0. 0. 0. 0.
	  secondary_response_mapping = 0. 0. 0. 0. 1. 0. 0. 0.
				       0. 0. 0. 0. 0. 0. 0. 1.

method,
	id_method = 'UQ_UNCERTAIN_EXP'
	model_pointer = 'UQ_M_HI_FI'
	stoch_collocation
	  quadrature_order = 3			#s0,#s1,#s2,#s3
#	  sparse_grid_level = 2 non_nested	#s4,#s5,#s6,#s7,#s8
	  num_response_levels = 0 1 1
	  response_levels = 0.0 0.0
	  compute reliabilities
	  complementary distribution

#################################
# begin UQ model specifications #
#################################
# NOTE: must have different models to enable different variables views
model,
	id_model = 'UQ_M_LO_FI'
	single
	  variables_pointer = 'UQ_V_LO_FI'
	  interface_pointer = 'UQ_I'
	  responses_pointer = 'UQ_R_LO_FI'

model,
	id_model = 'UQ_M_HI_FI'
	single
	  variables_pointer = 'UQ_V_HI_FI'
	  interface_pointer = 'UQ_I'
	  responses_pointer = 'UQ_R_HI_FI'

variables,
	id_variables = 'UQ_V_LO_FI'
	active all				#s0,#s1,#s4,#s5
	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'

variables,
	id_variables = 'UQ_V_HI_FI'
	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 = 'mod_cantilever'
#	  deactivate restart_file evaluation_cache

responses,
	id_responses = 'UQ_R_LO_FI'
	response_functions = 3
	no_gradients				#s0,#s1,#s4,#s5
#	analytic_gradients			#s2,#s3,#s6,#s7,#s8
	no_hessians

responses,
	id_responses = 'UQ_R_HI_FI'
	response_functions = 3
	analytic_gradients
	no_hessians