scipy優化器optimizer

#optimazer優化器
from scipy.optimize import minimize
def rosem(x):
    return sum(100.0*(x[1:]-x[:-1])**2.0+(1-x[:-1])**2.0)
x0=np.array([1.3,.7,.8,1.9,1.2])
res=minimize(rosem,x0,method="nelder-mead",options={"xtol":1e-8,"disp":True})
#method="nelder-mead"優化器采用的算法；"xtol":1e-8為精度；"disp":True顯示中間過程
print("rose mini:",res)#求函數最小值
print("rose mini:",res.x)
#有約束條件的求最小值，目標值
def func(x):
    return (2*x[0]*x[1]+2*x[0]-x[0]**2-2*x[1]**2)
def func_derive(x):#求導數
    dfdx0=(-2*x[0]+2*x[1]+2)#x0的導數 x[0]
    dfdx1 = (2 * x[0] -4* x[1])  # x1的導數 x[1]
    return np.array([dfdx0,dfdx1])
constriate=({"type":"eq","fun":lambda x：},"jac":lambda x:,#等式約束
                    "ineq", "fun": lambda x：}, "jac": lambda x:)#不等式約束
res=minimize(func,[-1,1],jac=func_derive,constraints=constriate,method='SLSQP',
       options={"xtol":1e-8,"disp":True})#jac為雅可比行列式，constraints約束條件，method算法
#求根
from scipy.optimize import root
def fun(x):
    return x**2
sol=root(fun,0.1)#求根，賦值0.1
print (sol.x,sol.fun)#sol.x為根，sol.fun為根的值