﻿ Python illustrating Downhill simplex method for minimizing the user-supplied scalar function_python_开心洋葱网
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# Python illustrating Downhill simplex method for minimizing the user-supplied scalar function

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Python illustrating Downhill simplex method for minimizing the user-supplied scalar function

```''' x = downhill(F,xStart,side,tol=1.0e-6)
Downhill simplex method for minimizing the user-supplied
scalar function F(x) with respect to the vector x.
xStart = starting vector x.
side   = side length of the starting simplex (default is 0.1)
'''
from numpy import zeros,dot,argmax,argmin,sum
from math import sqrt

def downhill(F,xStart,side=0.1,tol=1.0e-6):
n = len(xStart)                 # Number of variables
x = zeros((n+1,n))
f = zeros(n+1)

# Generate starting simplex
x = xStart
for i in range(1,n+1):
x[i] = xStart
x[i,i-1] = xStart[i-1] + side
# Compute values of F at the vertices of the simplex
for i in range(n+1): f[i] = F(x[i])

# Main loop
for k in range(500):
# Find highest and lowest vertices
iLo = argmin(f)
iHi = argmax(f)
# Compute the move vector d
d = (-(n+1)*x[iHi] + sum(x,axis=0))/n
# Check for convergence
if sqrt(dot(d,d)/n) < tol: return x[iLo]

# Try reflection
xNew = x[iHi] + 2.0*d
fNew = F(xNew)
if fNew <= f[iLo]:        # Accept reflection
x[iHi] = xNew
f[iHi] = fNew
# Try expanding the reflection
xNew = x[iHi] + d
fNew = F(xNew)
if fNew <= f[iLo]:    # Accept expansion
x[iHi] = xNew
f[iHi] = fNew
else:
# Try reflection again
if fNew <= f[iHi]:    # Accept reflection
x[iHi] = xNew
f[iHi] = fNew
else:
# Try contraction
xNew = x[iHi] + 0.5*d
fNew = F(xNew)
if fNew <= f[iHi]: # Accept contraction
x[iHi] = xNew
f[iHi] = fNew
else:
# Use shrinkage
for i in range(len(x)):
if i != iLo:
x[i] = (x[i] - x[iLo])*0.5
f[i] = F(x[i])
print "Too many iterations in downhill"
print "Last values of x were"
return x[iLo]
```

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