[SciPy optimization tutorial] v. quick solution of univariate function optimization

Refer to the official website ：Scipy.

Many times we just need a single variable function （ That is, a function with scalar input ） The minimum value of . under these circumstances , Some methods can be optimized faster . All of these can be obtained from minim_scalar Get in function , It proposes several algorithms .

Unconstrained minimization (method=‘brent’)

actually , There are two ways to minimize univariate functions ：Brent and golden, but golden Just for academic purposes , Rarely used . These two methods can be achieved by minim_scalar Select the method parameters in .brent Use Brent's algorithm to locate the minimum .
Examples are as follows ：

from scipy.optimize import minimize_scalar
f = lambda x: (x - 2) * (x + 1)**2
res = minimize_scalar(f, method='brent')
print(res.x)

The result is 1

Bounded minimization (method=‘bounded’)

For example $x=5$ Search near $J_1(x)$ The minimum value of .minimize_scalar It can be limited to $[4,7]$ Between , The result is $x_{min}=5.3314$

from scipy.special import j1
from scipy.optimize import minimize_scalar
res = minimize_scalar(j1, bounds=(4, 7), method='bounded')
print(res.x)