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#!/usr/bin/env python
import nlopt
import numpy as np
print ('nlopt version='+nlopt.__version__)
def f(x, grad):
F=x[0]
L=x[1]
E=x[2]
I=x[3]
D=F*L**3/(3.*E*I)
return D
n = 4
opt = nlopt.opt(nlopt.LN_COBYLA, n)
opt.set_min_objective(f)
lb = np.array([40., 50., 30e3, 1.])
ub = np.array([60., 60., 40e3, 10.])
x = (lb+ub)/2.
opt.set_lower_bounds(lb)
opt.set_upper_bounds(ub)
opt.set_xtol_rel(1e-3)
opt.set_ftol_rel(1e-3)
xopt = opt.optimize(x)
opt_val = opt.last_optimum_value()
result = opt.last_optimize_result()
print ('opt_result='+str(result))
print ('optimizer='+str(xopt))
print ('opt_val='+str(opt_val))
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