/usr/share/pyshared/openopt/solvers/scipy_optim/scipy_cobyla_oo.py is in python-openopt 0.34+svn1146-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | from scipy.optimize import fmin_cobyla
import openopt
from openopt.kernel.setDefaultIterFuncs import *
from openopt.kernel.ooMisc import WholeRepr2LinConst, xBounds2Matrix
from openopt.kernel.baseSolver import baseSolver
from numpy import inf, array, copy
#from openopt.kernel.setDefaultIterFuncs import SMALL_DELTA_X, SMALL_DELTA_F
class EmptyClass: pass
class scipy_cobyla(baseSolver):
__name__ = 'scipy_cobyla'
__license__ = "BSD"
__authors__ = """undefined"""
__alg__ = "Constrained Optimization BY Linear Approximation"
__info__ = 'constrained NLP solver, no user-defined derivatives are handled'#TODO: add '__info__' field to other solvers
__optionalDataThatCanBeHandled__ = ['A', 'Aeq', 'b', 'beq', 'lb', 'ub', 'c', 'h']
funcForIterFcnConnection = 'f'
def __init__(self): pass
def __solver__(self, p):
#p.kernelIterFuncs.pop(SMALL_DELTA_X)
#p.kernelIterFuncs.pop(SMALL_DELTA_F)
xBounds2Matrix(p)
p.cobyla = EmptyClass()
if p.userProvided.c: p.cobyla.nc = p.c(p.x0).size
else: p.cobyla.nc = 0
if p.userProvided.h: p.cobyla.nh = p.h(p.x0).size
else: p.cobyla.nh = 0
det_arr = cumsum(array((p.cobyla.nc, p.cobyla.nh, p.b.size, p.beq.size, p.cobyla.nh, p.beq.size)))
cons = []
for i in range(det_arr[-1]):
if i < det_arr[0]:
c = lambda x, i=i: - p.c(x)[i] # cobyla requires positive constraints!
elif det_arr[0] <= i < det_arr[1]:
j = i - det_arr[0]
c = lambda x, j=j: p.h(x)[j]
elif det_arr[1] <= i < det_arr[2]:
j = i - det_arr[1]
#assert 0<= j <p.cobyla.nb
c = lambda x, j=j: p.b[j] - p.dotmult(p.A[j], x).sum() # cobyla requires positive constraints!
elif det_arr[2] <= i < det_arr[3]:
j = i - det_arr[2]
#assert 0<= j <p.cobyla.nbeq
c = lambda x, j=j: p.dotmult(p.Aeq[j], x).sum() - p.beq[j]
elif det_arr[3] <= i < det_arr[4]:
j = i - det_arr[3]
c = lambda x, j=j: - p.h(x)[j]
elif det_arr[4] <= i < det_arr[5]:
j = i - det_arr[4]
#assert 0<= j <p.cobyla.nbeq
c = lambda x, j=j: p.dotmult(p.Aeq[j], x).sum() - p.beq[j]
else:
p.err('error in connection cobyla to openopt')
cons.append(c)
## def oo_cobyla_cons(x):
## c0 = -p.c(x)
## c1 = p.h(x)
## c2 = -(p.matmult(p.A, x) - p.b)
## c3 = p.matmult(p.Aeq, x) - p.beq
## return hstack((c0, c1, -c1, c2, c3, -c3))
# p.xk = p.x0.copy()
# p.fk = p.f(p.x0)
#
# p.iterfcn()
# if p.istop:
# p.xf = p.xk
# p.ff = p.fk
# return
xf = fmin_cobyla(p.f, p.x0, cons = tuple(cons), iprint = 0, maxfun = p.maxFunEvals, rhoend = p.xtol )
p.xk = xf
p.fk = p.f(xf)
p.istop = 1000
# p.iterfcn()
# p.xf = xf
# p.ff = p.fk
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