#@ *: DakotaConfig=HAVE_NPSOL
## DAKOTA INPUT FILE - dakota_svanberg_sbo_vlb5.in
#                      lower bound on x5 prevents hard convergence unless Lagrange multiplier accounts for bounds
#  The Svanberg five-segment beam has a linear objective and a nonlinear constraint:
#  Svanberg, Krister, "The Method of Moving Asymptotes--A New Method for
#  Structural Optimization," Intl. J. Num. Meth. Vol. 24, 1987, pp. 359-373.

environment,
	tabular_data
	tabular_data_file = 'dakota_svanberg_dakslp.dat'	#s0
#	tabular_data_file = 'dakota_svanberg_tana.dat'   	#s1
	method_pointer = 'SBLO'

variables,
	continuous_design = 5
	  initial_point  5.0  5.0  5.0  5.0  5.0
	  lower_bounds   0.0  0.0  0.0  0.0  2.2
	  upper_bounds  10.0 10.0 10.0 10.0 10.0
	  descriptors   'x1' 'x2' 'x3' 'x4' 'x5'

interface,
	id_interface = 'TRUE_FN'
	fork
	  analysis_driver = 'svanberg'
	  parameters_file = 'svanberg_params.in'
	  results_file = 'svanberg_results.out'
	  deactivate active_set_vector
#	  file_tag file_save
	
method,
	id_method = 'SBLO'
	surrogate_based_local
	model_pointer = 'SURROGATE'
	approx_method_pointer = 'SAO'
	max_iterations = 50
	  convergence_tolerance = 2e-3 # threshhold for hard convegence with correct Lagrange multiplier for x5=x5_lb
	  constraint_tolerance  = 1e-4
	trust_region
	  initial_size = 0.40
	  contraction_factor = 0.5
	  expansion_factor   = 2.0
	acceptance_logic  filter

method,                                         
	id_method = 'SAO'
	npsol
	  convergence_tolerance = 1e-4
	  constraint_tolerance  = 1e-5

model,
	id_model = 'SURROGATE'
	responses_pointer = 'SURROGATE_RESP'
 	surrogate local taylor_series		#s0
# 	surrogate multipoint tana			#s1
	  actual_model_pointer = 'TRUTH'

model,
	id_model = 'TRUTH'
	single
	  interface_pointer = 'TRUE_FN'
	  responses_pointer = 'TRUE_RESP'

responses,
	id_responses = 'SURROGATE_RESP'
	num_objective_functions = 1             
	nonlinear_inequality_constraints = 1
	analytic_gradients
	no_hessians

responses,                                      
	id_responses = 'TRUE_RESP'
	num_objective_functions = 1             
	nonlinear_inequality_constraints = 1
	analytic_gradients
	no_hessians