The clnlbeam problem
Based on an AMPL model by Hande Y. Benson
Copyright (C) 2001 Princeton University All Rights Reserved
Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that the copyright notice and this permission notice appear in all supporting documentation.
Source: H. Maurer and H.D. Mittelman, "The non-linear beam via optimal control with bound state variables", Optimal Control Applications and Methods 12, pp. 19-31, 1991.
using JuMP
import Ipopt
function example_clnlbeam()
N = 1000
h = 1/N
alpha = 350
model = Model(Ipopt.Optimizer)
@variables(model, begin
-1 <= t[1:(N + 1)] <= 1
-0.05 <= x[1:(N + 1)] <= 0.05
u[1:(N + 1)]
end)
@NLobjective(
model,
Min,
sum(
0.5 * h * (u[i + 1]^2 + u[i]^2) +
0.5 * alpha * h * (cos(t[i + 1]) + cos(t[i]))
for i = 1:N
),
)
@NLconstraint(
model,
[i = 1:N],
x[i + 1] - x[i] - 0.5 * h * (sin(t[i + 1]) + sin(t[i])) == 0,
)
@constraint(
model,
[i = 1:N],
t[i + 1] - t[i] - 0.5 * h * u[i + 1] - 0.5 * h * u[i] == 0,
)
optimize!(model)
println("""
termination_status = $(termination_status(model))
primal_status = $(primal_status(model))
objective_value = $(objective_value(model))
""")
return
end
example_clnlbeam()This is Ipopt version 3.13.2, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 8000
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 4002
Total number of variables............................: 3003
variables with only lower bounds: 0
variables with lower and upper bounds: 2002
variables with only upper bounds: 0
Total number of equality constraints.................: 2000
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.5000000e+02 0.00e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 3.5000000e+02 0.00e+00 0.00e+00 -1.7 0.00e+00 - 1.00e+00 1.00e+00 0
2 3.5000000e+02 0.00e+00 0.00e+00 -3.8 0.00e+00 -2.0 1.00e+00 1.00e+00T 0
3 3.5000000e+02 0.00e+00 0.00e+00 -5.7 0.00e+00 0.2 1.00e+00 1.00e+00T 0
4 3.5000000e+02 0.00e+00 0.00e+00 -8.6 0.00e+00 -0.2 1.00e+00 1.00e+00T 0
Number of Iterations....: 4
(scaled) (unscaled)
Objective...............: 3.5000000000000318e+02 3.5000000000000318e+02
Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 2.5059035596802450e-09 2.5059035596802450e-09
Overall NLP error.......: 2.5059035596802450e-09 2.5059035596802450e-09
Number of objective function evaluations = 5
Number of objective gradient evaluations = 5
Number of equality constraint evaluations = 5
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 5
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 4
Total CPU secs in IPOPT (w/o function evaluations) = 0.112
Total CPU secs in NLP function evaluations = 0.007
EXIT: Optimal Solution Found.
termination_status = LOCALLY_SOLVED
primal_status = FEASIBLE_POINT
objective_value = 350.0000000000032This page was generated using Literate.jl.