1#@ s*: Label=FastTest 2#@ *: DakotaConfig=HAVE_AMPL 3#@ *: DakotaConfig=HAVE_NPSOL 4# Solve the AMPL diet1 problem: minimize cost subject to buying at 5# least one entree, side, and drink. The optimal objective is 2.81. 6# 7# The AMPL model files are generated from pyomo: 8# pyomo diet1.py diet.dat --save-model dakota_ampl_diet.nl --symbolic-solver-labels 9# 10# This test is protecting parsing of AMPL labels with spaces and AMPL 11# responses that are ordered differently than the Dakota reponses. 12# 13# Technically the decision variables are discrete, but the SQP finds 14# the correct solution on the bounds of the domain 15 16method, 17 npsol_sqp 18 max_iterations = 50, 19 convergence_tolerance = 1e-12 20 21variables, 22 continuous_design = 9 23 initial_point 9 * 0.5 24 upper_bounds 9 * 1.0 25 lower_bounds 9 * 0.0 26 descriptors 27 'Buy[Sausage_McMuffin]' 28 'Buy[Orange_Juice]' 29 'Buy[Big_Mac]' 30 'Buy[Filet-O-Fish]' 31 'Buy[Quarter_Pounder_w_Cheese]' 32 'Buy[1%_Lowfat_Milk]' 33 'Buy[Fries,_small]' 34 'Buy[McGrilled_Chicken]' 35 'Buy[McLean_Deluxe_w_Cheese]' 36 37interface, 38 algebraic_mappings = 'dakota_ampl_diet.nl' 39 asynchronous 40 41responses, 42 objective_functions = 1 43 nonlinear_inequality_constraints = 3 44 lower_bounds = 3 * 1.0 45 upper_bounds = 3 * 20.0 46 response_descriptors = 47 'Total_Cost' 48 'Entree' 49 'Side' 50 'Drink' 51 analytic_gradients 52 no_hessians 53 54