1#@ *: DakotaConfig=HAVE_NPSOL 2# DAKOTA INPUT FILE - dakota_sbo_barnes.in 3 4# Demonstrates the use of approximation models and a trust region 5# optimization environment in performing constrained minimization on 6# the textbook test function. 7 8# These tests exercise global, local, and multipoint surrogates for 9# feasible and infeasible starting points with and without constraint 10# relaxation. 11 12# Test s16 is s14, but with new gauss_proc surrogate; worse 13# performance, but tests analytic gradient; hopefully will improve 14# once trend is added. 15 16environment, 17 method_pointer = 'SBLO' 18 19method, 20 id_method = 'SBLO' 21 surrogate_based_local 22 model_pointer = 'SURROGATE' 23 approx_method_pointer = 'NLP' 24 max_iterations = 50 25 trust_region 26 initial_size = 0.10 27 contraction_factor = 0.5 28# constraint_relax homotopy #s6,#s8,#s9,#s11 29 expansion_factor = 1.50 30 soft_convergence_limit = 1 # Slowed convergence criteria 31 minimum_size = 0.001 32 33method, 34 id_method = 'NLP' 35 npsol 36 max_iterations = 50 37 convergence_tolerance = 1e-4 38 39model, 40 id_model = 'SURROGATE' 41 responses_pointer = 'SURROGATE_RESP' 42 surrogate global #s0,#s2,#s3,#s5,#s6,#s8,#s9,#s11,#s14,#s15,#s16,#s17 43 dace_method_pointer = 'SAMPLING' #s0,#s2,#s3,#s5,#s6,#s8,#s9,#s11,#s14,#s15,#s16,#s17 44 polynomial quadratic #s0,#s2,#s3,#s5,#s6,#s8,#s9,#s11 45# gaussian_process surfpack #s14,#s15 46# experimental_gaussian_process #s16 47# find_nugget 1 #s16 48# trend none #s16 49# Emulate s2 with new surrogate poly: 50# experimental_polynomial #s17 51# basis_order 2 #s17 52# use_derivatives #s15 53 correction additive first_order #s0,#s3,#s6,#s9 54# surrogate local taylor_series #s1,#s4,#s7,#s10 55# actual_model_pointer = 'TRUTH' #s1,#s4,#s7,#s10 56# surrogate multipoint tana #s12,#s13 57# actual_model_pointer = 'TRUTH' #s12,#s13 58 59variables, 60 continuous_design = 2 61# Feasible starting point 62 initial_point 30. 40. #s0,#s1,#s2,#s3,#s4,#s5,#s12,#s14,#s15,#s16,#s17 63# Infeasible starting point 64# initial_point 65. 1. 65# Infeasible starting point 66# initial_point 10. 20. #s6,#s7,#s8,#s9,#s10,#s11,#s13 67 lower_bounds 0. 0. 68 upper_bounds 80. 80. 69 descriptors 'x1' 'x2' 70 71responses, 72 id_responses = 'SURROGATE_RESP' 73 objective_functions = 1 74 nonlinear_inequality_constraints = 3 75 nonlinear_inequality_lower_bounds = 0. 0. 0. 76 nonlinear_inequality_upper_bounds = 1.e+50 1.e+50 1.e+50 77 analytic_gradients 78 no_hessians 79 80############################################### 81# SAMPLING method specifications for building # 82# surrogate function(s) # 83############################################### 84method, 85 id_method = 'SAMPLING' 86 model_pointer = 'TRUTH' 87 sampling 88 seed = 12345 samples = 10 89 90model, 91 id_model = 'TRUTH' 92 single 93 interface_pointer = 'TRUE_FN' 94 responses_pointer = 'TRUE_RESP' 95 96interface, 97 direct #system #asynchronous 98 id_interface = 'TRUE_FN' 99 analysis_driver = 'barnes' 100 101responses, 102 id_responses = 'TRUE_RESP' 103 objective_functions = 1 104 nonlinear_inequality_constraints = 3 105 nonlinear_inequality_lower_bounds = 0. 0. 0. 106 nonlinear_inequality_upper_bounds = 1.e+50 1.e+50 1.e+50 107# no_gradients #s2,#s8,#s14,#s16,#s17 108# numerical_gradients #s7 109# method_source dakota #s7 110 analytic_gradients #s0,#s1,#s3,#s4,#s5,#s6,#s9,#s10,#s11,#s12,#s13,#s15 111 no_hessians 112