Question

我试图使用Python找到一个名为f的函数的根y。

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中用函数fsolve找到它。

是：

[0.3901,0.6166,0.0038,0.0202,0.2295,0.1076,0.0223]

我真的想使用Python。任何人都可以解释为什么Python中的scipy.optimize.root在matlab中的fsolve不会收敛吗？

有关信息，scipy.optimize.solve也不会收敛。

Answer 1

尝试不同的方法。对我来说，method="lm"（我猜Levenberg-Marquardt，但我不完全确定）效果很好：

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)

这会产生：

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]

我实际上有点惊讶Levenberg-Marquardt不是默认的。它通常是人们尝试的第一个“梯度下降”风格解算器之一。

当Matlab fsolve工作时，Scipy.optimize.root不会在Python中收敛，为什么？

1 个答案: