gtol=gtol,
diffInt=diffInt,
ftol=ftol,
maxIter=1390,
plot=PLOT,
color=colors[j],
iprint=10,
df_iter=4,
legend=solver,
show=False,
contol=contol,
maxTime=maxTime,
maxFunEvals=maxFunEvals,
name="NLP_bench_1",
)
p.constraints = [c1 < 0, c2 < 0, h1.eq(0), h2.eq(0), x > lb, x < ub]
# p.constraints = h1.eq(0)
# p._Prepare()
# print p.dc(p.x0)
# print h1.D(startPoint)
# print h2.D(startPoint)
# continue
if solver == "algencan":
p.gtol = 1e-2
elif solver == "ralg":
pass
# p.debug = 1
# p.debug = 1
###############################################################
solvers = ['ralg', 'scipy_cobyla', 'lincher', 'scipy_slsqp', 'ipopt','algencan']
#solvers = ['ralg', 'ipopt']
solvers = ['ralg', 'scipy_cobyla', 'lincher', 'scipy_slsqp', 'ipopt']
solvers = ['ralg', 'scipy_slsqp', 'scipy_cobyla', 'algencan']
#solvers = ['ipopt','ralg', 'algencan']
solvers = ['ralg', 'scipy_cobyla']
#solvers = ['ralg', 'scipy_slsqp']
#solvers = ['ralg', 'algencan']
solvers = ['ralg']
###############################################################
lines, results = [], {}
for j, solver in enumerate(solvers):
p = NLP(ff, startPoint, xlabel = Xlabel, gtol=gtol, diffInt = diffInt, ftol = ftol, maxIter = 1390, plot = PLOT, color = colors[j], iprint = 10, df_iter = 4, legend = solver, show=False, contol = contol, maxTime = maxTime, maxFunEvals = maxFunEvals, name='NLP_bench_1')
p.constraints = [c1<0, c2<0, h1.eq(0), h2.eq(0), x > lb, x< ub]
#p.constraints = h1.eq(0)
#p._Prepare()
#print p.dc(p.x0)
#print h1.D(startPoint)
#print h2.D(startPoint)
#continue
if solver =='algencan':
p.gtol = 1e-2
elif solver == 'ralg':
pass
#p.debug = 1
#p.debug = 1
from FuncDesigner import *
from openopt import NLP
a, b, c = oovars('a', 'b', 'c')
f = sum(a * [1, 2])**2 + b**2 + c**2
startPoint = {a: [100, 12], b: 2, c: 40}
p = NLP(f, startPoint)
p.constraints = [(2*c+a-10)**2<1.5, (a-10)**2<1.5, a[0]>8.9, a+b>[ 7.97999836, 7.8552538 ], \
a<9, (c-2)**2<1, b<-1.02, c>1.01, log2(c-15*a/b)+a>4, ((b+2*c-1)**2).eq(0)]
r = p.solve('ralg', iprint=1)
print r.xf
# {a: array([ 8.99999769, 8.87525255]), b: array([-1.0199994]), c: array([ 1.01000874])}
from FuncDesigner import *
from openopt import NLP
a, b, c = oovars('a', 'b', 'c')
f = sum(a*[1, 2])**2+b**2+c**2
startPoint = {a:[100, 12], b:2, c:40} # however, you'd better use numpy arrays instead of Python lists
p = NLP(f, startPoint)
p.constraints = [(2*c+a-10)**2 < 1.5 + 0.1*b, (a-10)**2<1.5, a[0]>8.9, (a+b > [ 7.97999836, 7.8552538 ])('sum_a_b', tol=1.00000e-12), \
a < 9, ((c-2)**2 < 1), (b < -1.02), c > 1.01, ((b + c * log10(a).sum() - 1) ** 2==0)(tol=1e-6)]
r = p.minimize('ralg', plot=0, xtol=1e-7)
#r = p.solve('ralg') # for NLPs old-style (openopt 0.25 and below) p.solve() is same to p.minimize()
#r = p.maximize('ralg')
print(r.xf)
print(a(r.xf))
a_opt, b_opt, c_opt = r(a, b, c)
# or any of the following:
# a_opt, b_opt, c_opt = r(a), r(b), r(c)
# r('a'), r('b'), r('c') (provided you have assigned the names to oovars as above)
# r('a', 'b', 'c')
# a(r), b(r), c(r)
"""
Expected output:
...
objFunValue: 717.75631 (feasible, max constraint = 7.44605e-07)
{a: array([ 8.99999792, 8.87525277]), b: array([-1.01999971]), c: array([ 1.0613562])}
"""
from FuncDesigner import *
from openopt import NLP
a, b, c = oovars('a', 'b', 'c')
f = sum(a*[1, 2])**2+b**2+c**2
startPoint = {a:[100, 12], b:2, c:40}
p = NLP(f, startPoint)
p.constraints = [(2*c+a-10)**2<1.5, (a-10)**2<1.5, a[0]>8.9, a+b>[ 7.97999836, 7.8552538 ], \
a<9, (c-2)**2<1, b<-1.02, c>1.01, log2(c-15*a/b)+a>4, ((b+2*c-1)**2).eq(0)]
r = p.solve('ralg', iprint =1)
print r.xf
# {a: array([ 8.99999769, 8.87525255]), b: array([-1.0199994]), c: array([ 1.01000874])}
from FuncDesigner import *
from openopt import NLP, oosolver
from numpy import arange
N = 10
a = oovar('a', size=N)
b = oovar('b', size=N)
f = (sum(abs(a*arange(1,N+1))**2.5)+sum(abs(b*arange(1,N+1))**1.5)) / 10000
startPoint = {a:cos(arange(N)), b:sin(arange(N))} # however, you'd better use numpy arrays instead of Python lists
p = NLP(f, startPoint)
#p.constraints = [(a>1)(tol=1e-5), a**2+b**2 < 0.1+a+b]# + [(a[i]**2+b[i]**2 < 3+abs(a[i])+abs(b[i])) for i in range(N)]
p.constraints = [a>1, 10*a>10] # + [(a[i]**2+b[i]**2 < 3+abs(a[i])+abs(b[i])) for i in range(N)]
#p.constraints = []
#C = sum(a**2+b**2 - (0.1+a+b)
C = [ifThenElse(a[i]**2+b[i]**2 - (0.1+a[i]+b[i])<0, 0, a[i]**2+b[i]**2 - (0.1+a[i]+b[i])) for i in range(N)]
p.constraints.append(sum(C)<0)
solver = 'ipopt'
solver = oosolver('ralg')
#solver = oosolver('ralg', approach='nqp')
r = p.minimize(solver, maxIter = 15000)
