The first instruction asks for maximization, whereas the later parts refer to minimization. For the literal maximization task, minimize the negative objective:
import numpy as np
from scipy import optimize as opt
def f(x):
return 1 + np.exp(-x*x/5) * np.sin(5*x)
maximum = opt.minimize(lambda z: -f(z[0]), x0=[-1.0])
print(maximum.x[0], f(maximum.x[0]))opt.minimize(lambda z: f(z[0]), x0=[-1.0]), which instead returns a local minimum near , . The optimizer's stopping criterion establishes local numerical convergence, not a certificate of a global optimum. Articles by others on the same topic
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