/usr/share/pyshared/openopt/tests/nlpLC.py is in python-openopt 0.34+svn1146-1build1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | from openopt import NLP
from numpy import cos, arange, ones, asarray, zeros, mat, array
N = 100
# objective function:
f = lambda x: ((x-5)**2).sum()
# objective function gradient (optional):
df = lambda x: 2*(x-5)
# start point (initial estimation)
x0 = 8*cos(arange(N))
# c(x) <= 0 constraints
def c(x):
r = zeros(N-1)
for i in xrange(N-1):
r[i] = x[i]**2 + x[i+1]**2 - 1
return r
def dc(x):
r = zeros((N-1, N))
for i in xrange(N-1):
r[i, i] = 2*x[i]
r[i, i+1] = 2*x[i+1]
return r
# counter = 0
# r = zeros(N*(N-1)/2)
# for i in xrange(N):
# for j in xrange(i):
# r[counter] = x[i]**2 + x[j]**2 - 1
# counter += 1
# return r
lb = -6*ones(N)
ub = 6*ones(N)
ub[4] = 4
lb[5], ub[5] = 8, 15
# general linear inequality constraints
#A = zeros((3, N))
#A[0, 9] = 1
#A[0, 19] = 1
#A[1, 10:12] = 1
#A[2] = -ones(N)
#b = [7, 9, -825]
A, b=None, None
# general linear equality constraints
#Aeq = zeros(N)
#Aeq[10:12] = 1
#beq = 11
Aeq, beq = None, None
# required constraints tolerance, default for NLP is 1e-6
contol = 1e-7
# If you use solver algencan, NB! - it ignores xtol and ftol; using maxTime, maxCPUTime, maxIter, maxFunEvals, fEnough is recommended.
# Note that in algencan gtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
gtol = 1e-7 # (default gtol = 1e-6)
# Assign problem:
# 1st arg - objective function
# 2nd arg - start point
p = NLP(f, x0, df=df, c=c, dc=dc,
gtol=gtol, contol=contol, iprint = 50, maxIter = 10000, maxFunEvals = 1e7, name = 'NLP_1')
#p = NLP(f, x0, df=df, Aeq=Aeq, beq=beq,
# gtol=gtol, contol=contol, iprint = 50, maxIter = 10000, maxFunEvals = 1e7, name = 'NLP_1')
#p = NLP(f, x0, df=df, lb=lb, ub=ub, gtol=gtol, contol=contol, iprint = 50, maxIter = 10000, maxFunEvals = 1e7, name = 'NLP_1')
#optional: graphic output, requires pylab (matplotlib)
p.plot = 0
#optional: user-supplied 1st derivatives check
p.checkdf()
p.checkdc()
p.checkdh()
def MyIterFcn(p):
return 0
# print 'Iteration',p.iter
# if p.iter == 50:
# p.user.mylist.append(p.xk.copy())
# return 0
p.user.mylist = []
# solve the problem
#p.debug=1
solver = 'algencan'
solver = 'ralg'
#solver = 'ipopt'
#solver = 'scipy_slsqp'
p.debug=1
r = p.solve(solver, showLS=0, iprint=10, maxTime = 50, callback = MyIterFcn) # string argument is solver name
#r = p.solve('r2', iprint = 1, plot=0, showLS=1, maxIter=480)
# r.xf and r.ff are optim point and optim objFun value
# r.ff should be something like 132.0522
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