实现用python的optimize库的fsolve对非线性方程组进行求解。可以看到这一个问题实际上还是一个优化问题，也可以用之前拟合函数的leastsq求解。下面用这两个方法进行对比：
from scipy.optimize import fsolve,leastsqfrom math import sin,cos def f(x): x0 = float(x[0]) x1 = float(x[1]) x2 = float(x[2]) return [ 5*x1+3, 4*x0*x0 - 2*sin(x1*x2), x1*x2-1.5 ] x0 = [1,1,1]result = fsolve(f,x0) print("===================")print()print("求解函数名称:",fsolve.__name__)print("解：",result)print("各向量值：",f(result))#拟合函数来求解h = leastsq(f,x0) print("===================")print()print("求解函数名称:",leastsq.__name__)print("解：",h[0])print("各向量的值：",f(h[0])) ===================
求解函数名称: fsolve
解： [-0.70622057 -0.6 -2.5 ]
各向量值： [0.0, -9.126033262418787e-14, 5.329070518200751e-15]
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求解函数名称: leastsq
解： [-0.70622057 -0.6 -2.5 ]
各向量的值： [0.0, -2.220446049250313e-16, 0.0]
