Matlab fsolve工作时,Scipy.optimize.root无法在Python中收敛,为什么? [英] Scipy.optimize.root does not converge in Python while Matlab fsolve works, why?
问题描述
我正在尝试使用Python查找名为f的函数的根y.
I am trying to find the root y of a function called f using Python.
这是我的代码:
def f(y):
w,p1,p2,p3,p4,p5,p6 = y[:7]
t1 = w - 0.99006633*(p1**0.5) - (-1.010067)*((1-p1))
t2 = w - 22.7235687*(p2**0.5) - (-1.010067)*((1-p2))
t3 = w - 9.71323491*(p3**0.5) - (-1.010067)*((1-p3))
t4 = w - 2.43852877*(p4**0.5) - (-1.010067)*((1-p4))
t5 = w - 3.93640207*(p5**0.5) - (-1.010067)*((1-p5))
t6 = w - 9.22688144*(p6**0.5) - (-1.010067)*((1-p6))
t7 = p1 + p2 + p3 + p4 + p5 + p6 - 1
return [t1,t2,t3,t4,t5,t6,t7]
x0 = np.array([-0.01,0.4,0.1,0.2,0.1,0.1,0.1])
sol = scipy.optimize.root(f, x0)
print sol
Python找不到根.但是有一个,我在Matlab中找到了fsolve函数.
Python does not find the root. However there is one, I found it with the function fsolve in Matlab.
是:
[0.3901,0.6166,0.0038,0.0202,0.2295,0.1076,0.0223]
[ 0.3901, 0.6166, 0.0038, 0.0202, 0.2295, 0.1076, 0.0223]
我真的很想使用Python.谁能解释为什么Python中的scipy.optimize.root无法在Matlab中的fsolve收敛?
I really want to use Python. Can anyone explain why scipy.optimize.root in Python does not converge while fsolve in Matlab does?
有关信息,scipy.optimize.solve也不会收敛.
For info, scipy.optimize.solve does not converge either.
推荐答案
尝试其他方法.对我来说,method="lm"
(我猜是Levenberg-Marquardt,但我不确定),效果很好:
Try a different method. For me, method="lm"
(I'm guessing Levenberg-Marquardt, but I'm not entirely sure) works very well:
import numpy as np
import scipy.optimize
def f(y):
w,p1,p2,p3,p4,p5,p6 = y[:7]
t1 = w - 0.99006633*(p1**0.5) - (-1.010067)*((1-p1))
t2 = w - 22.7235687*(p2**0.5) - (-1.010067)*((1-p2))
t3 = w - 9.71323491*(p3**0.5) - (-1.010067)*((1-p3))
t4 = w - 2.43852877*(p4**0.5) - (-1.010067)*((1-p4))
t5 = w - 3.93640207*(p5**0.5) - (-1.010067)*((1-p5))
t6 = w - 9.22688144*(p6**0.5) - (-1.010067)*((1-p6))
t7 = p1 + p2 + p3 + p4 + p5 + p6 - 1
return [t1,t2,t3,t4,t5,t6,t7]
x0 = np.array([-0.01,0.4,0.1,0.2,0.1,0.1,0.1])
sol = scipy.optimize.root(f, x0, method='lm')
assert sol['success']
print 'Solution: ', sol.x
print 'Misfit: ', f(sol.x)
这将产生:
Solution: [ 0.39012036 0.61656436 0.00377616 0.02017937 0.22954825
0.10763827 0.02229359]
Misfit: [0.0, 0.0, 1.1102230246251565e-16, -1.1102230246251565e-16,
1.1102230246251565e-16, 0.0, -2.2204460492503131e-16]
我实际上对Levenberg-Marquardt不是默认值感到有些惊讶.通常,它是最早尝试的梯度下降"样式求解器之一.
I'm actually a bit surprised Levenberg-Marquardt isn't the default. It's usually one of the first "gradient-descent" style solvers one would try.
这篇关于Matlab fsolve工作时,Scipy.optimize.root无法在Python中收敛,为什么?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!