bench 3 - interalg

from FuncDesigner import *
from openopt import *
from numpy import zeros

n = 7
a = oovars(n)
F = sum([cos(10 * (i+1) * a[i])+(a[i]-0.01*(i+1))**2 for i in range(n)])
startPoint = {a:zeros(n)}
constraints = [a[i]>0 for i in range(n)] + [a[i]<1 for i in range(n)]
p = GLP(F, startPoint, fTol = 1e-2, iterObjFunTextFormat='%0.9e', constraints = constraints)
r = p.minimize('interalg')


Output:
--------------------------------------------------
solver: interalg_0.17   problem: unnamed    type: GLP
 iter    objFunVal
    0  7.014000000e+00
   10  -2.748630494e+00
   20  -6.126417785e+00
   30  -6.649460106e+00
OpenOpt info: Solution with required tolerance 1.0e-02
 is guarantied (obtained precision: 9.988e-03)
   40  -6.877908181e+00
istop: 1000 (optimal solution obtained)
Solver:   Time Elapsed = 26.62  CPU Time Elapsed = 26.38
objFunValue: -6.8779082 (feasible, MaxResidual = 0)