1#@ s*: Label=FastTest 2#@ s2: DakotaConfig=HAVE_DOT 3#@ s3: DakotaConfig=HAVE_DOT 4# DAKOTA INPUT FILE - dakota_illum.in 5 6# This sample Dakota input file optimizes the illumination example 7# taken from course notes taught by Prof. Stephen Boyd at Stanford. 8# 9# the "hero solution" for the illumination problem found by using 10# ncsu_direct and then polishing that answer with conmin_frcg is 11# <<<<< Best parameters = 12# 1.0000000000e+00 x1 13# 2.8186100902e-01 x2 14# 0.0000000000e+00 x3 15# 0.0000000000e+00 x4 16# 0.0000000000e+00 x5 17# 7.5621311116e-01 x6 18# 1.0000000000e+00 x7 19# <<<<< Best objective function = 20# 1.0759888860e+00 21 22 23method, 24 optpp_q_newton, #s0 25# optpp_newton #s5 26# optpp_pds, #s1 27# dot_bfgs, #s2 28# dot_frcg, #s3 29# conmin_frcg, #s4 30 max_iterations = 50, 31 convergence_tolerance = 1e-4 32# scaling #s2,#s3 33 34variables, 35 continuous_design = 7 36 initial_point .5 .5 .5 .5 .5 .5 .5 37 lower_bounds 0. 0. 0. 0. 0. 0. 0. #s0,#s2,#s3,#s4,#s5 38 upper_bounds 1. 1. 1. 1. 1. 1. 1. #s0,#s2,#s3,#s4,#s5 39 descriptors 'x1' 'x2' 'x3' 'x4' 'x5' 'x6' 'x7' 40# scale_type = 'value' #s2,#s3 41# scales = 7 * .5 #s2 42# scales = 7 * .1 #s3 43 44interface, 45 direct 46 analysis_drivers = 'illumination' 47 48responses, 49 objective_functions = 1 50# no_gradients #s1 51 numerical_gradients #s0,#s2,#s3,#s4 52 method_source dakota #s0,#s2,#s3,#s4 53 interval_type central #s0,#s2,#s3,#s4 54 fd_gradient_step_size = .000001 #s0,#s2,#s3,#s4 55 no_hessians #s0,#s1,#s2,#s3,#s4 56# analytic_gradients #s5 57# analytic_hessians #s5 58