Assume the following function:
f(x) = x * cos(x-4)
With x = [-2.5, 2.5]
this function crosses 0
at f(0) = 0
and f(-0.71238898) = 0
.
This was determined with the following code:
import math
from scipy.optimize import fsolve
def func(x):
return x*math.cos(x-4)
x0 = fsolve(func, 0.0)
# returns [0.]
x0 = fsolve(func, -0.75)
# returns [-0.71238898]
What is the proper way to use fzero
(or any other Python root finder) to find both roots in one call? Is there a different scipy
function that does this?
fzero
reference
Find the roots of a function. Return the roots of the (non-linear) equations defined by func(x) = 0 given a starting estimate. A function that takes at least one (possibly vector) argument, and returns a value of the same length.
I once wrote a module for this task. It's based on chapter 4.3 from the book Numerical Methods in Engineering with Python by Jaan Kiusalaas:
import math
def rootsearch(f,a,b,dx):
x1 = a; f1 = f(a)
x2 = a + dx; f2 = f(x2)
while f1*f2 > 0.0:
if x1 >= b:
return None,None
x1 = x2; f1 = f2
x2 = x1 + dx; f2 = f(x2)
return x1,x2
def bisect(f,x1,x2,switch=0,epsilon=1.0e-9):
f1 = f(x1)
if f1 == 0.0:
return x1
f2 = f(x2)
if f2 == 0.0:
return x2
if f1*f2 > 0.0:
print('Root is not bracketed')
return None
n = int(math.ceil(math.log(abs(x2 - x1)/epsilon)/math.log(2.0)))
for i in range(n):
x3 = 0.5*(x1 + x2); f3 = f(x3)
if (switch == 1) and (abs(f3) >abs(f1)) and (abs(f3) > abs(f2)):
return None
if f3 == 0.0:
return x3
if f2*f3 < 0.0:
x1 = x3
f1 = f3
else:
x2 =x3
f2 = f3
return (x1 + x2)/2.0
def roots(f, a, b, eps=1e-6):
print ('The roots on the interval [%f, %f] are:' % (a,b))
while 1:
x1,x2 = rootsearch(f,a,b,eps)
if x1 != None:
a = x2
root = bisect(f,x1,x2,1)
if root != None:
pass
print (round(root,-int(math.log(eps, 10))))
else:
print ('\nDone')
break
f=lambda x:x*math.cos(x-4)
roots(f, -3, 3)
roots
finds all roots of f
in the interval [a
, b
].
Define your function so that it can take either a scalar or a numpy array as an argument:
>>> import numpy as np
>>> f = lambda x : x * np.cos(x-4)
Then pass a vector of arguments to fsolve
.
>>> x = np.array([0.0, -0.75])
>>> fsolve(f,x)
array([ 0. , -0.71238898])
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