e5dbe416
 Chunk
staged.
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__author__ = 'chunk'
import numpy as np
import math
def deriv(fn):
dx = 0.00001
return lambda x: (fn(x + dx) - fn(x)) / dx
def dampnm(fun, x0, lmbda, epsilon, gfun=None):
assert lmbda < 1 and lmbda > 0
if gfun == None:
gfun = deriv(fun)
x_new = x0
x_old = None
# while np.absolute(fun(x_new)) > epsilon:
while x_old == None or np.absolute(fun(x_new) - fun(x_old)) > epsilon:
x_old = x_new
s = fun(x_old) / gfun(x_old)
x_new = x_old - s
while np.absolute(fun(x_new)) >= np.absolute(fun(x_old)):
x_new = x_old - lmbda * s
lmbda /= 2.0
print lmbda, x_new
return x_new
def test():
fn = lambda x: math.pow(x, 4) - x - 2
gfn = lambda x: 4 * math.pow(x, 3) - 1
print dampnm(fn, 1.5, 0.99, 0.00001, gfn)
fn = lambda x: math.pow(x, 3) - x - 1
gfn = lambda x: 3 * math.pow(x, 2) - 1
print dampnm(fn, 0.6, 0.99, 0.00001, gfn)
fn = lambda x: -math.pow(x, 3) + 5 * x
gfn = lambda x: -3 * math.pow(x, 2) + 5
print dampnm(fn, 1.2, 0.99, 0.00001, gfn)
# 0.99 1.375
# 0.99 1.35377701579
# 0.99 1.35321036029
# 0.99 1.3532099642
# 1.3532099642
# 0.01546875 1.13521875
# 0.01546875 1.36976137194
# 0.01546875 1.32649943078
# 0.01546875 1.32472090757
# 0.01546875 1.32471795725
# 0.01546875 1.32471795724
# 1.32471795724
# 0.2475 -1.90976470588
# 0.2475 -2.34458457506
# 0.2475 -2.24316474291
# 0.2475 -2.23610151441
# 0.2475 -2.23606797825
# 0.2475 -2.2360679775
# -2.2360679775
if __name__ == '__main__':
test()
pass
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