from scipy.optimize import minimize

`scipy.optimize.minimize` is a function that provides a unified interface for minimizing various types of functions using different optimization algorithms. It takes a function to minimize, an initial guess for the minimum, and optionally, any additional arguments that are required by the function.

Here's an example usage of `scipy.optimize.minimize`:

import numpy as np
from scipy.optimize import minimize

# Define the function to minimize
def rosen(x):
    return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)

# Set the initial guess
x0 = np.array([0.5, 1.6, -0.8, 1.8, 0.7])

# Minimize the function using the L-BFGS-B algorithm
res = minimize(rosen, x0, method='L-BFGS-B')

# Print the result
print(res.x)

In this example, we define the Rosenbrock function `rosen` to minimize, set the initial guess `x0`, and then use the L-BFGS-B algorithm to minimize the function. The result is printed as `res.x`.