scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
except:
sign = 1.0
x = d[0]
y = d[1]
return sign*(2*x*y + 2*x - x**2 - 2*y**2)
def testfunc_deriv(d,*args):
""" This is the derivative of testfunc, returning a numpy array
representing df/dx and df/dy
"""
try:
sign = args[0]
except:
sign = 1.0
x = d[0]
y = d[1]
dfdx = sign*(-2*x + 2*y + 2)
dfdy = sign*(2*x - 4*y)
return array([ dfdx, dfdy ],float)
from time import time
print ’\n\n’
print "Unbounded optimization. Derivatives approximated."
t0 = time()
x = fmin_slsqp(testfunc, [-1.0,1.0], args=(-1.0,), iprint=2, full_output=1)
print "Elapsed time:", 1000*(time()-t0), "ms"
print "Results",x
print "\n\n"
print "Unbounded optimization. Derivatives provided."
t0 = time()
x = fmin_slsqp(testfunc, [-1.0,1.0], args=(-1.0,), iprint=2, full_output=1)
print "Elapsed time:", 1000*(time()-t0), "ms"
print "Results",x
print "\n\n"
print "Bound optimization. Derivatives approximated."
t0 = time()
x = fmin_slsqp(testfunc, [-1.0,1.0], args=(-1.0,),
eqcons=[lambda x, y: x[0]-x[1] ], iprint=2, full_output=1)
print "Elapsed time:", 1000*(time()-t0), "ms"
print "Results",x
print "\n\n"
print "Bound optimization (equality constraints). Derivatives provided."
t0 = time()
x = fmin_slsqp(testfunc, [-1.0,1.0], fprime=testfunc_deriv, args=(-1.0,),
eqcons=[lambda x, y: x[0]-x[1] ], iprint=2, full_output=1)
print "Elapsed time:", 1000*(time()-t0), "ms"
print "Results",x
print "\n\n"
print "Bound optimization (equality and inequality constraints)."
print "Derivatives provided."
22 Chapter 1. SciPy Tutorial