1.5.12.1. Finding the minimum of a smooth functionΒΆ

Demos various methods to find the minimum of a function.

import numpy as np
import matplotlib.pyplot as plt
def f(x):
return x**2 + 10*np.sin(x)
x = np.arange(-10, 10, 0.1)
plt.plot(x, f(x))
../../../_images/sphx_glr_plot_optimize_example1_001.png

Now find the minimum with a few methods

from scipy import optimize
# The default (Nelder Mead)
print(optimize.minimize(f, x0=0))

Out:

fun: -7.945823375615215
hess_inv: array([[ 0.08589237]])
jac: array([ -1.19209290e-06])
message: 'Optimization terminated successfully.'
nfev: 18
nit: 5
njev: 6
status: 0
success: True
x: array([-1.30644012])
print(optimize.minimize(f, x0=0, method="L-BFGS-B"))

Out:

fun: array([-7.94582338])
hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
jac: array([ -1.42108547e-06])
message: b'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'
nfev: 12
nit: 5
status: 0
success: True
x: array([-1.30644013])

Total running time of the script: ( 0 minutes 0.016 seconds)

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